<h2>AI Is a Multiplier, Not a Replacement — And the Difference Is Judgment</h2>Insights from Trâm Nguyễn, AI Engineer at VMOThere's a common fear circulating through boardrooms and engineering teams alike: AI is coming for the jobs. But what if that framing is fundamentally wrong?For Trâm Nguyễn, an AI Engineer at VMO, the real story is far more nuanced - and far more empowering. She doesn't see AI as a threat to engineering. She sees it as a force multiplier. And the difference between who wins and who stalls in the AI era, she argues, comes down to one thing: judgment.<h2>The Backlog Nobody Wanted to Touch</h2>Every enterprise has one. A list of internal tools, automations, and workflow improvements that everyone knows they need - but nobody ever gets around to building.These aren't headline projects. They're the niche-but-critical items: a form that still lives in someone's inbox, a reporting process still trapped in a spreadsheet, an approval chain that still runs on email threads and institutional memory. Too specific to buy off the shelf as SaaS, too small to justify spinning up a dedicated dev team, they just sit there - year after year."Most enterprises had a backlog of items that they want to build but they consider those really small but important," Trâm explains. "These items are either too specific to buy as SaaS or too numerous to staff a dev team for each one. So they stayed stuck in spreadsheets, email threads or paper forms."Traditionally, if something did get built, it was slow and expensive - long lead times, big budgets, and a gauntlet of prioritization battles. Most items never made it out of the backlog at all.That's changing.<h2>Compressing the Distance Between Idea and Production</h2>AI hasn't just made coding faster. It has fundamentally collapsed the timeline between identifying a problem and shipping a working solution.<blockquote>"AI compresses the distance between idea and production stage applications. The work that used to require a multi-person team and take months to develop now can be executed by a single AI-accelerated engineer - from end-to-end design, build, integration, testing." - Trâm Nguyễn, AI Engineer at VMO</blockquote>This is a structural shift, not just a productivity boost. Work that once demanded cross-functional teams, months of planning, and substantial budgets can now be scoped, built, and shipped by a single engineer who knows how to work with AI effectively.For enterprises, this means the backlog isn't a permanent fixture anymore. The niche items - the ones that still rely heavily on manual process without audit trail or real-time visibility - can finally get built.<h2>The Engineer's Role Has Shifted - But It Hasn't Disappeared</h2>So if AI can do the heavy lifting, does the engineer become redundant?Not even close. The engineer's role has evolved, not evaporated."The engineer's job has shifted from writing the code manually to directing the build, validating AI output and taking ownership of the parts that AI cannot be trusted," Trâm says.This is a meaningful distinction. AI can generate code quickly. What it cannot do is know whether that code is safe to ship, whether it meets governance standards, or whether a subtle architectural decision will create a liability six months down the line. That's where the engineer steps in - not to write every line, but to direct, validate, and own the outcome.<h2>Same Tool. Very Different Outcomes.</h2>Here's where Trâm makes her most important point - one that cuts through a lot of the hype around "AI democratizing development."Yes, anyone can open a coding assistant and get a demo running. But getting a demo and shipping a production-grade system are not the same thing.<blockquote>"A non-technical person and an AI full stack engineer may have the same access to the exact same tool, but fundamentally they produce different outcomes. A non-technical person can get to a demo, but it stalls. But an AI full stack engineer knows how to ship a system, because they know what to build, how to construct, and how to catch AI mistakes before they become liabilities." - Trâm Nguyễn, AI Engineer at VMO</blockquote>This is the multiplier effect in action. Give the same AI tool to a non-engineer and an AI-accelerated engineer, and you don't get the same result - you get a prototype versus a production system. The tool amplifies existing judgment. It doesn't create judgment from scratch.AI can quickly generate a demo, but it stalls the moment something breaks. It has no way to diagnose why it's breaking, no ability to get itself unstuck. Without engineering intuition behind the wheel, the output remains permanently at the demo stage.<h2>A Real-World Example: One Engineer, Enterprise-Grade Delivery</h2>To illustrate this, Trâm points to a recent client delivery at VMO.The project involved replacing a fully manual process with a digitalized, governed workflow - one that had to meet enterprise constraints from day one: multilingual support, multi-region compliance rules, corporate identity standards. Under traditional development timelines, this would have been a multi-month, multi-person effort.With an AI-accelerated approach, a single engineer delivered a production-grade system - not a prototype.<blockquote>"AI can quickly give you a demo, but I think it stalls because AI has no way to diagnose why it's breaking or get itself unstuck. Engineering alone without AI can get you the correctness but at the old pace and cost. When you combine the two together, an engineer alone can produce the output of the whole traditional team." -Trâm Nguyễn, AI Engineer at VMO</blockquote>The result wasn't just faster. It was also compliant with the hard constraints that enterprises can't compromise on - governance, auditability, scalability. These aren't afterthoughts you bolt on later; they're baked in from the start when an experienced engineer is directing the build.<h2>Governance Doesn't Have to Be the Enemy of Speed</h2>One of the persistent tensions in enterprise software development has been the tradeoff between speed and governance. Move fast and you accumulate technical debt and compliance risk. Move carefully and you fall behind.AI, deployed with engineering judgment, dissolves this tradeoff."AI also makes it feasible to meet the enterprise hard constraints from day one - such as corporate identity, multi-lingual, multi-region rules - without forcing the teams to choose between speed or governance," Trâm notes.This is a significant unlock. It means enterprises can finally stop treating governance as a brake on innovation and start treating it as a built-in feature of modern AI-assisted development.<h2>The Real Competitive Edge</h2>As AI tools become more widely available, the question isn't who has access to them. Everyone will. The question becomes: who can use them to consistently deliver governed, production-grade systems - versus who stalls at the demo?<blockquote>"AI supplies raw speed, but the engineer is the one that supplies the judgment — which decides what is correct and what is safe to ship. It's going to be who can take the same tool to consistently produce governed and production-grade systems instead of just a demo." - Trâm Nguyễn, AI Engineer at VMO</blockquote>That judgment - knowing what to build, how to structure it, how to catch AI mistakes before they compound - is the skill that separates a prototype from a product. And it's the skill that VMO's AI engineers bring to every engagement.<h2>Closing Thought: Multiply the Right Things</h2>AI is a multiplier. But a multiplier only makes things bigger — it doesn't make them better by default. Multiply poor judgment and you get poor outcomes, faster. Multiply strong engineering judgment and you get production-grade systems at a pace that was previously impossible.The backlog is finally addressable. The tradeoff between speed and governance is finally resolvable. The gap between demo and production is finally closeable.But only when the right judgment is in the driver's seat.Trâm Nguyễn is an AI Engineer at VMO, specializing in AI-accelerated enterprise application development. VMO (part of &nbsp;ALTEN Group) delivers technology solutions across software development, digital transformation, and emerging tech verticals.Want to learn more about how VMO is applying AI engineering to real enterprise challenges?Get in touch with our team

There's a common fear circulating through boardrooms and engineering teams alike: AI is coming for the jobs. But what if that framing is fundamentally wrong?

AI Is a Multiplier, Not a Replacement — And the Difference Is Judgment

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Healthcare transformation is often associated with hospitals, technology companies, and government agencies. Yet across the world, another powerful force is quietly shaping the future of healthcare access: religious organizations.From rural clinics operated by churches in Africa to telehealth initiatives supported by Islamic charities in Southeast Asia and digital health outreach programs led by Buddhist organizations, faith-based institutions are increasingly becoming essential partners in expanding healthcare access.As digital health technologies continue to evolve, religious organizations are uniquely positioned to accelerate adoption, particularly among underserved populations where trust, accessibility, and cultural understanding remain major barriers.<h2>The Overlooked Healthcare Infrastructure</h2>Religious institutions represent one of the world's largest and most established community networks.According to the World Health Organization, faith-based organizations provide a substantial portion of healthcare services in many low- and middle-income countries. In some regions of Sub-Saharan Africa, they account for between 30% and 70% of healthcare delivery.Their influence extends beyond medical facilities. Religious organizations often maintain:<ul><li>Extensive networks of local congregations.</li><li>Deep relationships with communities.</li><li>Volunteer and social support systems.</li><li>Educational institutions and charitable programs.</li><li>Strong credibility among vulnerable populations.</li></ul>This infrastructure creates a unique opportunity for digital health initiatives to reach populations that traditional healthcare systems sometimes struggle to engage.<h2>Why Trust Matters in Digital Health Adoption</h2>Technology alone does not guarantee better healthcare outcomes.Many digital health programs fail because communities hesitate to adopt unfamiliar technologies, particularly when concerns around privacy, misinformation, or cultural beliefs arise.Trust remains one of the strongest predictors of healthcare engagement.Faith leaders often serve as trusted advisors within their communities, influencing decisions related to health, wellbeing, and caregiving. When religious organizations endorse or participate in digital health programs, communities may become more willing to:<ul><li>Use telemedicine services.</li><li>Participate in remote patient monitoring programs.</li><li>Adopt mobile health applications.</li><li>Receive preventive health education.</li><li>Share health information digitally.</li><li>Engage in vaccination or screening initiatives.</li></ul>The result is not simply greater technology usage, but stronger and more sustainable healthcare engagement.<h2>Real-World Examples of Faith-Driven Digital Health</h2><h3>Telehealth in Rural and Underserved Communities</h3>Faith-operated clinics frequently serve remote populations where physician shortages remain significant.By integrating telemedicine platforms, these organizations can connect local patients with specialists located hundreds or even thousands of miles away.For many patients, receiving care through a trusted local faith-based facility significantly reduces barriers associated with travel, cost, and healthcare hesitancy.<h3>Mobile Health Programs Through Community Networks</h3>Religious gatherings provide consistent opportunities for health education and digital engagement.Several organizations globally have begun leveraging SMS platforms, mobile applications, and digital communication channels to distribute:<ul><li>Preventive health information.</li><li>Medication reminders.</li><li>Vaccination schedules.</li><li>Maternal health guidance.</li><li>Disease outbreak alerts.</li></ul>These initiatives transform existing faith communities into effective public health communication networks.<h3>Digital Mental Health Support</h3>Mental health remains heavily stigmatized in many societies.Faith leaders are often the first individuals community members approach when facing emotional distress, anxiety, grief, or depression.Such collaborations can improve early intervention while ensuring care remains culturally and spiritually sensitive.<h2>Southeast Asia: A Region of Significant Opportunity</h2>Southeast Asia presents a particularly compelling environment for faith-enabled digital health innovation.Religious institutions, including temples, churches, mosques, and faith-based charities, often maintain extensive grassroots networks that reach communities beyond the formal healthcare system.As governments across Southeast Asia continue investing in digital health ecosystems, partnerships with religious organizations may help accelerate adoption among populations that remain underserved or digitally hesitant.Examples of potential initiatives include:<ul><li>Community telehealth hubs hosted within religious facilities.</li><li>AI-powered health education programs delivered through faith networks.</li><li>Digital screening campaigns for chronic diseases.</li><li>Remote care programs for elderly populations supported by volunteers.</li><li>Mobile platforms for maternal and preventive healthcare.</li></ul><h2>Challenges and Considerations</h2>While the potential is significant, successful collaboration requires thoughtful implementation.Healthcare providers and technology organizations should consider several factors:<h3>Protecting Privacy and Data Security</h3>Faith-based healthcare programs must maintain rigorous standards for patient confidentiality and regulatory compliance, particularly when handling sensitive health information.<h3>Respecting Cultural and Religious Diversity</h3>Digital health solutions should be designed with cultural competence in mind. Programs must remain inclusive and ensure that healthcare delivery is evidence-based and non-discriminatory.<h3>Training and Digital Literacy</h3>Volunteers, caregivers, and faith leaders may require training to effectively support digital health initiatives and guide community members in using new technologies.<h3>Building Sustainable Partnerships</h3>Long-term success depends on collaboration among healthcare providers, governments, technology companies, and religious organizations rather than isolated interventions.<h2>What This Means for Healthcare Technology Companies</h2>For healthcare technology providers, faith-based organizations represent more than community partners; they are strategic enablers of healthcare equity.Organizations developing digital health platforms, telemedicine solutions, patient engagement applications, and AI-powered care models should consider how faith communities can support:<ul><li>User adoption and engagement.</li><li>Community outreach.</li><li>Preventive health campaigns.</li><li>Patient education.</li><li>Chronic disease management.</li><li>Last-mile healthcare delivery.</li></ul>As healthcare increasingly shifts toward patient-centric and community-based models, partnerships built on trust may become just as important as the technologies themselves.<h2>Conclusion</h2>The future of healthcare access will not be driven by technology alone. It will depend on trusted relationships, community engagement, and culturally relevant care delivery. Religious organizations have long served as pillars of support, compassion, and service within communities worldwide.By combining these strengths with digital health innovation, faith-based organizations can help close healthcare gaps, improve health equity, and ensure that digital transformation reaches those who need it most.In the intersection between faith and technology lies an often-overlooked opportunity: creating healthcare systems that are not only more connected but also more human-centered.

When Faith Meets Healthcare: How Religious Organizations Are Accelerating Digital Health Access

How Religious Organizations Are Accelerating Digital Health Access

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From Data to Decisions: How ALTEN is Shaping the Future of Manufacturing&nbsp;The manufacturing landscape is undergoing a seismic shift. No longer confined to passive data collection, factories are evolving into autonomous, AI-driven ecosystems capable of real-time decision-making. Yet, as Elia Brunelli, Technical Solution Manager at ALTEN Italy, highlights, the true challenge lies not in technological prowess alone, but in bridging the gap between innovation and practical deployment."Technology enables. Usability delivers."&nbsp;This article explores how ALTEN is tackling the core obstacles, such as legacy infrastructure, data quality, and skills gaps, to transform proofs-of-concept into scalable, human-centric solutions.What is the biggest paradigm shift in manufacturing today?The most profound transformation is the move from reactive monitoring to autonomous, AI-driven decision-making. Factories are no longer just collecting data; they’re deploying systems that detect anomalies, correct processes, and log interventions. This isn’t just an upgrade; it’s a fundamental rethinking of how production lines operate.For example, predictive maintenance was once seen as the ultimate goal. Today, it’s merely the starting point. The real breakthrough is creating systems that don’t just predict failures but autonomously resolve them, turning data into actionable clarity for operators.Why do so many industries struggle to scale these innovations?Three critical blockers persist: legacy infrastructure, data quality, and human expertise.First, legacy systems, many plants still rely on 20-year-old PLCs never designed for cloud integration. Retrofitting them without halting production is costly and complex.Second, data quality, AI is only as effective as the data it consumes. Most plants have partial, inconsistent, or poorly labelled sensor data, creating a bottleneck for AI adoption.Finally, people, deploying a model is straightforward but empowering technicians to master it is not. Currently, 60% of manufacturing tech potential remains untapped because the complexity exceeds operators’ skills. At ALTEN, we focus on designing solutions for the end user, not just the engineers who build them.How does ALTEN address these challenges?We prioritise human-centric design. For instance, in cobot orchestration projects, we built connected assembly lines where operators monitor and configure systems without needing to understand the underlying AI.Similarly, our IoT-based anomaly detection solutions process machine signals in real time, translating complex data into simple, decision-ready insights. The shift isn’t just from reactive to predictive maintenance; it’s from confusion to clarity.What technologies are unlocking these advancements?The convergence of edge computing, digital twins, and large language models is key. Edge computing is now affordable, digital twins have matured, and LLMs bridge the gap between operational systems and human operators. None of these are new, but their simultaneous readiness is revolutionary.For example, our "Mini Plant 4.0" combines a drone assembly line with a digital twin, allowing clients to simulate and optimise processes before deployment. This isn’t just innovation, it’s practical and scalable transformation.What skills will define the future of manufacturing?Technical literacy and domain knowledge remain vital, but soft skills such as creativity, collaboration, resilience are the differentiators. In hybrid environments where humans and autonomous systems coexist, these skills enable adaptation, innovation, and leadership.At ALTEN, we’re committed to upskilling teams to navigate this transition, ensuring technology serves people.&nbsp;About expert&nbsp;&nbsp; &nbsp; Elia Brunelli&nbsp;Technical Solution Manager at ALTEN Italy&nbsp; &nbsp; Elia Brunelli, leads the charge in transforming manufacturing through AI-driven, human-centric solutions. With expertise in Industry 5.0, he tackles challenges like legacy systems and skills gaps, ensuring technology is both innovative and practical. His projects, such as "Mini Plant 4.0," demonstrate how cobots, IoT, and digital twins can revolutionise production while empowering operators. Elia champions upskilling and collaboration, making advanced manufacturing accessible and effective.Connect with Elia Brunelli: <a href="https://it.linkedin.com/in/elia-brunelli-3880a5aa">LinkedIn profile</a>&nbsp;FooterNeed more insight on AI, cobots, and human expertise in the future of manufacturing?&nbsp; &nbsp; <a href="mailto:marketing@alten.com">Contact our expert</a>

Discover how ALTEN bridges AI, cobots, and human expertise to unlock the full potential of Industry 5.0. Insights from Elia Brunelli.

How ALTEN is Shaping the Future of Manufacturing

From Data to Decisions: How ALTEN is Shaping the Future of Manufacturing

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<h3>Agentic AI is emerging as one of the most significant technological shifts in modern banking. By combining reasoning, planning, and autonomous execution, it enables financial institutions to improve decision-making, streamline operations, strengthen risk management, and unlock new opportunities for sustainable growth.</h3><h2>1. From AI assistance to autonomous decision-making</h2>Agentic AI represents more than the next generation of artificial intelligence, it marks a fundamental shift in how financial institutions operate and make decisions.Unlike traditional AI, which relies on predefined rules, or Generative AI, which primarily responds to user prompts, Agentic AI consists of autonomous systems capable of planning, reasoning, using external tools, and executing multi-step actions to achieve user-defined objectives with minimal human intervention.This distinction is particularly important in banking. As financial markets become increasingly dynamic and customer expectations continue to evolve, decision-making can no longer rely solely on historical data or static business rules. Agentic AI enables banks to process structured and unstructured information simultaneously, adapt to changing market conditions, personalize financial services, and support real-time decision-making at scale.<h2>2. Building blocks and operating architecture of Agentic AI</h2>An Agentic AI system is not a single model but an integrated ecosystem composed of five core components that work together to enable autonomous decision-making.<ul><li>Instructions:&nbsp;define the agent's objectives, responsibilities, operational boundaries, and regulatory constraints.</li><li>Tools: allow the agent to interact with external systems, including financial data platforms such as Bloomberg and FactSet, execute Python code, or connect directly with Order Management Systems (OMS).</li><li>Information retrieval: combines Retrieval-Augmented Generation (RAG) with web search capabilities, enabling agents to access both internal knowledge bases and the latest market intelligence.</li><li>Memory: enables the agent to retain both short-term conversational context and long-term organizational knowledge through vector databases, allowing decisions to become increasingly context-aware over time.</li><li>Guardrails: ensure that every recommendation or action complies with internal policies and regulatory requirements. For example, an agent can automatically block investment recommendations or transactions that exceed predefined portfolio risk limits.</li></ul>Depending on business objectives, these components can be organized into several workflow patterns.<ul><li>Prompt chaining executes sequential tasks, making it suitable for processes such as preparing credit assessment reports.</li><li>Routing directs requests to specialized agents based on their domain, such as equity research, credit analysis, or macroeconomic forecasting.</li><li>Parallelization&nbsp;allows multiple analytical tasks to run simultaneously, significantly reducing processing time for large-scale portfolio analysis.</li><li>Orchestrator-worker architectures divide complex objectives into specialized subtasks managed by dedicated AI agents.</li><li>Evaluator-Optimizer workflows introduce an additional validation layer, where one AI generates a solution while another independently reviews and refines the output before execution.</li></ul><h2>3. Applications in Financial decision making</h2>As Agentic AI matures, financial institutions are beginning to move beyond task automation toward autonomous financial decision support across several critical functions.<h3>Risk profiling and credit assessment</h3>Rather than relying exclusively on traditional scorecards, Agentic AI continuously analyzes behavioral signals and transaction data to produce dynamic credit risk profiles.This adaptive approach enables financial institutions to improve lending accuracy while reducing non-performing loans, with several studies reporting reductions of up to 12%.<h3>Predictive loan underwriting</h3>Instead of applying rigid approval thresholds, Agentic AI evaluates transaction histories, electronic documentation, and behavioral patterns to predict repayment capacity more accurately.This allows banks to dynamically optimize loan approvals, pricing strategies, and credit limits while balancing profitability and risk exposure.<h3>Intelligent treasury management</h3>Agentic AI can continuously monitor liquidity across multiple accounts, automatically initiate fund transfers, recommend reinvestment opportunities, and respond to changing cash flow conditions within milliseconds.Such capabilities significantly improve liquidity optimization while reducing operational overhead.<h3>Personalized portfolio construction</h3>By combining ESG screening, macroeconomic analysis, and client-specific investment objectives, Agentic AI enables wealth managers to build highly customized Separately Managed Accounts (SMAs) for high-net-worth clients at a scale that would be difficult to achieve manually.<h2>4. Challenges of implementing Agentic AI</h2>Despite its significant potential, Agentic AI also introduces new governance challenges that financial institutions must address carefully.One of the most notable observations is the emerging AI paradox. Although nearly 80% of financial institutions across Asia report adopting AI, a similar proportion of organizations globally acknowledge that these initiatives have yet to generate meaningful bottom-line impact.Several factors contribute to this gap.<ul><li>Goal misalignment: Autonomous agents may optimize for financial performance while inadvertently violating customer risk preferences or organizational objectives.</li><li>Regulatory compliance: Financial AI systems must comply with increasingly stringent regulations, including the EU AI Act and local data privacy requirements established by financial regulators.</li><li>Pilot Purgatory: Many AI initiatives remain confined to isolated pilot projects due to fragmented organizational structures and limited cross-functional collaboration.</li></ul>To overcome these challenges, financial institutions are increasingly adopting Compliance by Design, establishing centralized AI governance frameworks, maintaining enterprise-wide AI registries, and ensuring Human-in-the-Loop (HITL) oversight for high-impact decisions.<h2>5. The Roadmap for Vietnamese banks</h2>Within Vietnam's banking sector, AI adoption remains largely focused on customer service chatbots and basic fraud detection. However, several institutions have already demonstrated meaningful progress.For example, Techcombank has applied AI to its credit assessment process, reducing loan approval time from three days to approximately six hours while improving operational productivity by around 15%. Importantly, human experts continue to perform the final review before approvals are issued, reflecting a balanced approach between automation and governance.To unlock the full potential of Agentic AI, Vietnamese banks can consider a three-stage adoption roadmap:<ul><li>Stage 1: Upgrade traditional chatbots into self-learning virtual assistants capable of continuously collecting customer behavioral data.</li><li>Stage 2: Expand AI into predictive credit scoring, intelligent fraud detection, and risk analytics.</li><li>Stage 3: Deploy Agentic AI across complex banking operations, including fully automated eKYC, intelligent investment advisory, and autonomous portfolio management.</li></ul>A key challenge remains the fragmented nature of financial data. Advancing toward an&nbsp;Open Banking ecosystem where banks securely integrate data from telecommunications providers, e-commerce platforms, and other trusted third parties will provide Agentic AI with richer contextual information and significantly improve prediction accuracy.<h2>6. VMO's Perspective: Turning Agentic AI into Enterprise Value</h2>At VMO, Agentic AI is viewed not as a replacement for human expertise, but as an intelligent execution layer that enables financial institutions to make faster, more informed, and more consistent decisions.Successful adoption begins with clearly identifying business processes where autonomous reasoning creates measurable value, followed by designing AI architectures that seamlessly integrate with existing banking systems while maintaining robust governance, security, and regulatory compliance.With extensive experience delivering AI solutions across the financial services sector, VMO supports banks and financial institutions throughout the entire AI transformation journey from strategy and solution architecture to deployment and continuous optimization. Rather than treating AI as an isolated technology initiative, VMO focuses on embedding AI into core business processes where it can create tangible operational value.Ultimately, the objective is not simply to automate banking operations, but to build trusted AI systems that enhance decision quality, operational efficiency, and customer experience simultaneously.<h2>7. Conclusion</h2>Agentic AI represents a significant evolution in enterprise AI from systems that generate information to systems capable of planning, reasoning, and acting autonomously.However, technology alone will not determine success. To translate AI investment into measurable business value, banks must establish executive-level commitment, strengthen collaboration across business, technology, and risk functions, modernize data infrastructure, and continuously develop an AI-ready workforce.The banks that lead the next decade will not necessarily be those deploying the largest AI models, but those that design the strongest operating model where autonomous AI and human expertise work together to make better financial decisions.

Agentic AI in Banking: Next Frontier of Intelligent Ops

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The way healthcare professionals learn is changing rapidly. Across the Asia-Pacific (APAC) region, medical schools, hospitals, and healthcare organizations are increasingly turning to AI and AR to modernize medical education.Traditional medical training, while foundational, often faces limitations in scalability, accessibility, and hands-on practice opportunities. AI and AR technologies are helping bridge these gaps by creating immersive, personalized, and data-driven learning experiences that better prepare clinicians for real-world practice.As healthcare systems across APAC continue to digitize, AI and AR-powered education is emerging as a strategic investment for institutions seeking to develop a future-ready workforce.<h2>Why Medical Education Needs Transformation</h2>Medical knowledge is expanding at an unprecedented pace. New treatments, technologies, and clinical guidelines are continuously introduced, requiring healthcare professionals to engage in lifelong learning.At the same time, healthcare organizations face several challenges:<ul><li>Limited access to clinical training opportunities.</li><li>Growing shortages of healthcare professionals.</li><li>Variations in training quality across regions.</li><li>Increasing complexity of procedures and technologies.</li><li>Rising demand for remote and flexible learning models.</li></ul>Conventional classroom-based approaches alone are no longer sufficient. Digital learning technologies are enabling institutions to provide more standardized, scalable, and engaging educational experiences.<h2>The Rise of AI in Medical Education</h2>AI is transforming medical education by making learning more adaptive, personalized, and efficient.<h3>Personalized Learning Pathways</h3>Every learner progresses differently. AI-powered learning platforms can analyze individual performance, identify knowledge gaps, and automatically tailor educational content to each student's needs.<h3>Intelligent Assessment and Feedback</h3>Traditional assessments often provide delayed feedback. AI systems can evaluate learner performance in real time and deliver immediate insights.Examples include:<ul><li>Automated scoring of clinical simulations.</li><li>AI-based assessment of procedural skills.</li><li>Speech and communication analysis during patient interactions.</li><li>Objective evaluation of surgical techniques.</li></ul>Instant feedback allows learners to continuously refine their skills and accelerate competency development.<h3>Virtual Patients and Clinical Decision Support</h3>AI-powered virtual patients simulate realistic clinical scenarios, enabling learners to practice diagnosis and treatment planning in a safe environment.Students can interact with virtual patients, review symptoms, order tests, and make treatment decisions while receiving guidance throughout the learning process.<h2>How AR Is Reshaping Hands-On Medical Training</h2>Augmented Reality overlays digital information onto the physical world, creating highly immersive educational experiences.Unlike traditional textbooks or videos, AR enables learners to visualize anatomy and procedures in three dimensions.<h3>Immersive Anatomy Education</h3>Understanding human anatomy is fundamental but often challenging.AR applications allow students to:<ul><li>Explore interactive 3D anatomical models.</li><li>Visualize organs from multiple angles.</li><li>Observe physiological processes dynamically.</li><li>Study complex structures without cadavers.</li></ul>Students can interact with holographic organs and systems, leading to deeper comprehension and improved engagement.<h3>Procedure and Surgical Simulation</h3>AR-based simulation platforms are increasingly used to train clinicians on complex procedures before they enter operating rooms.Examples include:<ul><li>Minimally invasive surgery training.</li><li>Catheter insertion practice.</li><li>Orthopedic procedure simulation.</li><li>Ultrasound-guided intervention training.</li></ul>By overlaying digital guidance onto physical models, AR enables repeated practice without risk to patients.<h3>Remote Collaboration and Mentoring</h3>Geographical barriers remain a significant challenge across APAC, particularly in rural and underserved areas.AR-enabled remote collaboration allows experts to guide trainees from different locations in real time.Specialists can:<ul><li>Annotate directly on the trainee's field of view.</li><li>Demonstrate procedures remotely.</li><li>Provide step-by-step guidance during simulations.</li><li>Support continuing medical education programs.</li></ul>This capability is particularly valuable in regions where specialist resources are limited.<h2>Innovation Trends Across Asia-Pacific</h2>The APAC region is becoming a global hub for digital health innovation, with governments, universities, and healthcare providers actively investing in advanced learning technologies.<h3>Singapore: Building Smart Healthcare Talent</h3>Singapore continues to lead digital healthcare adoption in Southeast Asia. Academic institutions and healthcare organizations are increasingly incorporating AI-driven simulations, virtual learning environments, and immersive technologies into medical training programs.The country's strong digital infrastructure and commitment to healthcare innovation make it a key market for next-generation medical education solutions.<h3>Australia: Expanding Simulation-Based Learning</h3>Australia has long been recognized for its advanced simulation centers. Universities and teaching hospitals are increasingly adopting AR and AI technologies to enhance clinical skills training, improve patient safety, and support remote education initiatives.The growing use of virtual patients and immersive procedural training reflects a broader shift toward competency-based education.<h3>Japan: Integrating Robotics, AI, and AR</h3>Japan's aging population and healthcare workforce challenges are accelerating investments in advanced training technologies.Medical institutions are exploring combinations of AI, robotics, AR, and mixed reality to train clinicians more efficiently while supporting continuous professional development.<h3>South Korea: Accelerating Digital Medical Innovation</h3>South Korea's strong technology ecosystem has fueled the rapid adoption of digital learning solutions.Medical schools and hospitals are leveraging AI-powered educational platforms and immersive visualization tools to enhance surgical education and precision medicine training.<h3>Emerging Opportunities in Southeast Asia</h3>Countries such as Vietnam, Thailand, Malaysia, and Indonesia are increasingly investing in digital health ecosystems.As healthcare demand rises, scalable and cost-effective training technologies offer significant potential to:<ul><li>Expand healthcare workforce capacity.</li><li>Improve training accessibility in remote regions.</li><li>Standardize clinical education.</li><li>Support continuing medical education initiatives.</li></ul><h2>Challenges to Adoption</h2>Despite strong momentum, organizations must address several challenges when implementing AI and AR in medical education.<h3>Data Privacy and Security</h3>Educational platforms handling learner and clinical data must comply with local regulatory requirements and maintain robust cybersecurity measures.<h3>Integration with Existing Systems</h3>Many institutions operate legacy learning management systems and hospital infrastructures that may require modernization or interoperability enhancements.<h3>Content Development Complexity</h3>Developing clinically accurate, high-quality immersive content requires close collaboration among healthcare professionals, educators, designers, and technology teams.<h3>Technology Accessibility</h3>Institutions must ensure that advanced learning technologies remain accessible, scalable, and cost-effective, particularly in resource-constrained settings.<h2>The Future of Medical Education in APAC</h2>The convergence of AI and AR is reshaping how healthcare professionals are educated across Asia-Pacific.Future learning environments are expected to become increasingly intelligent, immersive, and data-driven. Adaptive learning platforms, mixed reality simulations, digital twins, and automated competency assessments will likely become standard components of medical education ecosystems.As healthcare continues to evolve, organizations that invest in these technologies today will be better positioned to build highly skilled, future-ready workforces capable of delivering better patient outcomes.

AI + AR in Medical Education: Innovation Trends Across Asia-Pacific

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<h2>How Organizations Can Innovate with AI While Meeting Compliance, Security, and Trust Requirements</h2>AI is rapidly transforming industries worldwide. From automating clinical documentation in healthcare to accelerating risk assessment in financial services and improving quality control in manufacturing, AI is becoming a critical business capability.However, organizations operating in regulated industries face a unique challenge: how to harness the power of AI while maintaining strict security, compliance, privacy, and governance standards.For sectors such as healthcare, banking, insurance, life sciences, telecommunications, and government services, AI adoption cannot come at the expense of regulatory obligations. Security must be built into the foundation of every AI application, not added as an afterthought.This is why organizations are increasingly shifting their focus from simply deploying AI models to building secure, enterprise-grade AI solutions that are designed for compliance from day one.<h2>AI Introduces New Security Challenges</h2>Unlike traditional software, AI systems often process large volumes of sensitive data and generate dynamic outputs. This creates new risks, including data leakage, unauthorized access, model manipulation, and compliance violations.For example, healthcare AI applications may handle patient records, while financial AI systems analyze customer transactions and personal information. Any security failure can lead to regulatory penalties, reputational damage, and loss of customer trust.This makes security a fundamental requirement rather than an afterthought.<h2>Strong Data Governance Comes First</h2>Secure AI starts with strong data governance. Organizations need clear policies for how data is collected, stored, accessed, and used throughout the AI lifecycle.In regulated environments, maintaining control over sensitive information is critical. Effective governance helps ensure compliance, improves transparency, and reduces the risk of data misuse.Many AI initiatives succeed or fail based on the quality and governance of their data rather than the sophistication of the model itself.<h2>Embedding Security Throughout the AI Lifecycle</h2>Security should be integrated into every stage of AI development, from design and training to deployment and ongoing operations.Modern MLOps and LLMOps practices help organizations automate security checks, monitor model behavior, and maintain compliance without slowing innovation. By adopting a security-by-design approach, teams can identify risks early and build more resilient AI solutions.<h2>Transparency Builds Trust</h2>In regulated industries, organizations often need to explain how AI-driven decisions are made. Whether supporting clinical recommendations or fraud detection, AI systems must be transparent and auditable.Monitoring, logging, and governance mechanisms help organizations maintain visibility into model behavior while meeting regulatory expectations. Transparency not only supports compliance but also increases confidence among users, customers, and regulators.<h2>The Future of Enterprise AI</h2>As AI adoption grows, security and governance are becoming key differentiators. Organizations are moving beyond isolated AI experiments and investing in enterprise-grade platforms that combine data protection, access controls, model governance, and continuous monitoring.At the same time, responsible AI principles such as fairness, accountability, and explainability are becoming essential components of AI strategies.<h2>Conclusion</h2>Building AI applications for regulated industries requires far more than selecting the right model or technology stack. Success depends on creating secure, compliant, and governable systems that protect sensitive data, meet regulatory requirements, and maintain stakeholder trust.By adopting security-by-design principles, implementing robust governance frameworks, and integrating secure MLOps practices, organizations can confidently deploy AI solutions that drive business value without compromising compliance or security.As AI continues to reshape industries, the organizations that prioritize trust, security, and responsible innovation will be the ones that unlock its full potential.

Building Secure AI Applications for Regulated Industries

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By reading this article, you will gain valuable insights into the significant advantages of enlisting the expertise of a skilled SAP logistics consultant. It will provide a clear understanding of how such consultants can revolutionize logistics functions, leading to cost reduction, improved operational efficiency, and remarkable professional achievements.

SAP Logistics: Unlocking the Key Benefits, Strategies & Best Practices

This article explores the concepts of SAP logistics business processes and emphasizes the importance of logistics in supply chain optimization

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On April 1, 2021, VMO Holdings Technology Joint Stock Company (VMO) held a Ceremony to announce its Vision, Mission &amp; Core Values which has been solidified during the past 10 years of establishment and development. On that enduring journey full of proud milestones was the great leadership of the Board of Directors, in alignment with the consensus of all the members of VMO.&nbsp;Along with the outstanding development and growth, VMO embraces the goal of meeting an increasingly rigorous requirements of the IT market, conquering the satisfaction of significant customers in various large-scale projects. Above all lies the ultimate development goal of VMO, closely tied with its core values, lofty mission, and new vision to bring VMO to a new position, increase its influence and spread more good values to the community.In parallel with the investment and development of human resources and technology, VMO clearly understand the desires and ideal that company needs to implement to make VMO a global technology company. VMO officially announce its Mission, Vision &amp; Core values Statement:&nbsp;OUR VISION&nbsp;To be recognized as a Global Leading IT Company<ul><li>To actualize the innovative idea of clients around the world with professional software development and business consultation services.</li></ul>OUR MISSIONFrom Idea to Reality, From Goodwill to Impact<ul><li>VMO turn&nbsp;the&nbsp;ideas of the clients&nbsp;into valuable products that actively contribute for the betterment of the society.</li></ul>OUR CORE VALUESDesire<ul><li>Desire is the aspiration to achive greatness. We desire, we change, we thrive.</li></ul>Transparency<ul><li>&nbsp;VMO Holdings embraces transparency. Internally, we make sure that every voices are listened. To clients, we build the relationship with goodwill and trust. Transparency is the foundation of sustainable development.</li></ul>Conscientiousness<ul><li>We treat each task&nbsp;with&nbsp;passion and enthusiasm. Dedication is the secret of outstanding efficiency.</li></ul>Synergy&nbsp;<ul><li>We&nbsp;are group&nbsp;of&nbsp;different personalities with a same will&nbsp;and common goal. Synergies is the engine of great teamwork.</li></ul>VMO Holdings Technology Joint Stock Company hopes that a new vision, mission, and core values will serve to achieve company’s development goals, bring company to a new position, upgrade its corporate culture, and create useful values to contribute to the development of global humanity.

On April 1,  VMO officially announced vision, mission and Core values Statement

VMO ANNOUNCED VISION, MISSION, CORE VALUES

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On December 24, 2019, VMODEV Hanoi Technology Joint Stock Company officially established a branch of VMO Japan at 〒101-0047 Tokyo , Chiyoda-ku , Uchikanda, 2-14-12, Hoshiya 6th Building No 402, one of World's largest economic centers.&nbsp;Founded in 2012, VMODEV has over 8 years developing and accompanying many European and American businesses, successfully building many technological products from the concept stage to delivering to users, including the product reaches more than 15 million users worldwide.Quality, Flexibility, Speed are the core values that the leaders and employees of VMODEV have pursued for nearly 10 years. Identifying Japan as the next important market in the company's long-term development strategy, Mr. Hoang Tuan Hai, CEO of VMODEV said: “The Japanese IT market is a large market with strict requirements for quality. We believe that by understanding the culture of our team in Japan, VMODEV can completely bring success to Japanese businesses, such as the satisfaction we bring to clients in the Europe and America, EU markets”.In Japan, VMODEV wishes to assist clients in deploying digital business models, as well as applying IT platforms and new technologies such as AI, Cloud, RPA ... to improve productivity and efficiency for current business models. VMODEV will provide high quality IT human resources for Japanese businesses. It is expected that after Japan, VMODEV will continue to expand more branches in countries such as the USA, Singapore, Korea ... in the near future.&nbsp;More information about VMODEVVMODEV was founded in 2012 by technology enthusiasts and desires to contribute and bring practical values to society. Currently the company has 4 working offices and its headquarters are located in Hanoi, Vietnam. Over 8 years of working with many partners in Europe, America, EU, Asia ... VMODEV has developed more than 300 projects with clients from 11 countries. The products we design and deploy reach more than 45 million users globally.&nbsp;Contact information<figure class="table"><table><tbody><tr><td>VMODEV HANOI TECHNOLOGY JOINT STOCK COMPANYAddess: 15th Floor, TTC Tower, 19 Duy Tan st., Dich Vong Hau ward., Cau Giay dist., Hanoi 100000Phone number: +84 24 3312 0103Website: <a href="https://www.vmodev.com/">https://www.vmodev.com</a>Email: <a href="mailto:info@vmodev.com">info@vmodev.com</a></td><td>VMO JAPAN JOINT STOCK COMPANYAddress: 〒101-0047 Tokyo , Chiyoda-ku , Uchikanda, 2-14-12, Hoshiya 6th Building No 402Phone number: 03-4400-2818Website: <a href="https://www.vmodev.jp/">https://www.vmodev.jp</a>Email: <a href="mailto:infojp@vmodev.com">infojp@vmodev.com</a></td></tr></tbody></table></figure>&nbsp;

On December 24, 2019, VMODEV officially established a branch of VMO Japan

VMODEV OFFICIALLY ESTABLISHED A BRANCH IN JAPAN

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Job Description: We’re seeking for Pre-senior/Senior BA/DA to join our team&nbsp;Key Responsibilities<ul><li>Gather, analyse and formalise user’s business requirements and processes, evaluate on feasibility of implementation and manage scope of project.</li><li>Prepare project documentation such as Requirement Specifications/Use Case Specifications etc.</li><li>Adhere to project scope, update requirement traceability matrix, inform and record out-of-scope issues.</li><li>Develop technical design specification and translate technical design into process/program specification. Involve in the design review session.</li><li>Make changes to configurable items, which include hardware, software, documentation, etc.</li><li>Assist in the integration testing, system testing, User Acceptance Test &amp; implementation activities.</li><li>Prepare user manual and operations manual, and plan &amp; facilitate training of users and operators.</li><li>Provide post implementation support (including production support and service requests).</li><li>Escalate and discuss critical issues such as scope creep with the product managers.</li><li>Conduct requirement gathering sessions. Conduct gap analysis of current business process to identify improvement opportunities.</li><li>Collaborate with vendors to address integration challenges, fostering seamless system operation and reliability across systems used in the company.</li><li>Collaborate with stakeholders to enhance systems processes, ensuring alignment with overall business objectives.</li><li>Constantly explore opportunities to enhance systems and explore new tools in the market focusing on improving the efficiency and experience of users.</li><li>Provide technical training to a large audience from the user's perspective.</li><li>In-Depth Performance and Data Analysis for the system and data used by users.</li><li>Support multiple product and project streams, along with varying resources, across the team to ensure on time and quality delivery of all solutions.</li></ul>

Business Analyst

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About the Role&nbsp;:&nbsp;Responsible for managing end-to-end recruitment for positions in Telecom, Embedded, Wireless, C/C++, Network, and other specialized technical fields. Proactively develop candidate sources, perform talent mapping, and provide recruitment consultation to support the company’s strategic workforce needs.Key Responsibilities1. Recruitment<ul><li>Manage recruitment activities for positions including Telecom Engineer, Embedded Engineer, Wireless Engineer, C/C++ Engineer, Network Engineer, and related technical roles.&nbsp;</li><li>Handle the full recruitment lifecycle, including sourcing, screening, interviewing, offering, and onboarding.&nbsp;</li><li>Ensure achievement of recruitment KPIs in terms of quality, quantity, and turnaround time.&nbsp;</li></ul>2. Talent Mapping &amp; Market Intelligence<ul><li>Conduct talent mapping of target companies in Telecom, Embedded, and technology-related fields.&nbsp;</li><li>Research the talent market, competitors, and recruitment trends.&nbsp;</li><li>Build and maintain a pipeline of potential candidates to support long-term hiring needs.&nbsp;</li></ul>3. Stakeholder Management<ul><li>Work closely with Hiring Managers and business units to understand recruitment requirements.&nbsp;</li><li>Provide consultation on talent market insights, candidate availability, and appropriate recruitment strategies.&nbsp;</li><li>Track, update, and report recruitment progress on a regular basis.&nbsp;</li></ul>4. Sourcing &amp; Networking<ul><li>Proactively expand sourcing channels, networking activities, and referral networks.&nbsp;</li><li>Build candidate communities and maintain relationships with potential candidates in the market.</li></ul>

Talent Acquisition Specialist

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We are seeking a talented Data Scientist / AI Engineer with strong expertise in Generative AI, NLP, and LLM-based applications. The ideal candidate will have hands-on experience building end-to-end GenAI workflows and applying advanced AI technologies to solve real-world business problems.&nbsp;In This Role, You Will:&nbsp;<ul><li>Design and develop end-to-end Generative AI and NLP solutions.</li><li>Build and optimize LLM, RAG, Agentic AI, and NLP applications for business use cases.</li><li>Perform prompt engineering and develop scalable GenAI workflows.</li><li>Develop production-ready AI systems using Python and PySpark.</li><li>Collaborate closely with business and technical teams to deliver AI solutions with measurable impact.</li><li>Clearly communicate technical findings and recommendations to stakeholders.</li></ul>

Data Scientist - AIS

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Summary:&nbsp; The Data Scientist role focuses on building propensity models and sizing market opportunities for VMO project with banking clients. This hybrid position requires a blend of data science and engineering skills to create production-grade pipelines. The role is pivotal in driving sales strategy through data-driven outputs. Candidates should have a strong background in Python and SQL, along with proven experience in propensity modeling.Propensity Modeling &amp; Ranking: &nbsp; &nbsp; Predict CTR and engagement probability &nbsp; &nbsp; Use models like XGBoost / LightGBM &nbsp; &nbsp; Optimize ranking using NDCG, MRR, Precision@KFeature Engineering: &nbsp; &nbsp; Build features from user behavior, interaction signals, and context &nbsp; &nbsp; Handle sparse and noisy dataCold Start Handling: &nbsp; &nbsp; Design strategies for new users and new offers &nbsp; &nbsp; Implement hybrid and fallback approachesBias Handling: &nbsp; &nbsp; Mitigate popularity and exposure bias &nbsp; &nbsp; Implement diversity and re-ranking strategiesModel Evaluation: &nbsp; &nbsp; Define and track CTR, conversion, NDCG, AUC &nbsp; &nbsp; Continuously improve engagement metricsScalability &amp; Deployment: &nbsp; &nbsp; Build systems for millions of users &nbsp; &nbsp; Enable real-time or near real-time inference

AI Engineer - onsite

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<ul><li>Develop, execute, and maintain test plans, test cases, and test scripts for a digital platform.</li><li>Perform functional, regression, performance, integration, and user acceptance testing (UAT).</li><li>Collaborate with developers, business analysts, and designers to understand requirements and ensure test coverage.</li><li>Identify, document, and track defects using issue tracking tools (e.g., JIRA).</li><li>Validate bug fixes and perform re-testing as needed.</li><li>Participate in sprint planning, stand-ups, and retrospectives in an Agile environment.</li><li>Ensure digital products meet accessibility standards and cross-browser/device compatibility.</li><li>Develop and support automation testing efforts and contribute to test automation frameworks.</li><li>Provide QA sign-off for releases and deployments.</li></ul>

FULLSTACK TESTER (AUTOMATION & MANUAL) - ENG GOOD - DU33

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About the Role As an HR Business Partner (HRBP) Expert, you will act as a strategic advisor to key business units (Operations, Delivery, Sales, etc.), partnering closely with leadership to drive organizational effectiveness, talent development, and business performance. You will play a critical role in aligning people strategies with business objectives while fostering a high-performance and purpose-driven culture. Recruitment Requirements Key Responsibilities<ul><li>Partner with business leaders to design and implement people strategies that drive performance, engagement, and growth.</li><li>Advise on organizational design, workforce planning, and headcount management aligned with business priorities.</li><li>Collaborate with Talent Acquisition and L&amp;D teams to attract, develop, and retain key talents.</li><li>Lead talent management initiatives, including performance management, succession planning, and leadership development.</li><li>Drive career development frameworks, competency mapping, and capability building programs.</li><li>Act as a trusted advisor to leaders on employee relations and complex people matters.</li><li>Ensure compliance with local labor laws and internal policies.</li><li>Champion and embed company culture, ensuring alignment between organizational values and business practices.</li><li>Leverage HR data and analytics (attrition, engagement, performance) to generate insights and support decision-making.</li><li>Continuously improve HR processes to enhance efficiency and employee experience.</li></ul>

HR Business Partner Team Leader

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About the RoleWe are looking for a Senior DevOps Engineer with strong expertise in AWS, Kubernetes, and CI/CD to build and operate scalable cloud-native platforms. You will work closely with development teams to automate infrastructure, optimize deployment processes, and ensure system reliability, security, and performance.Responsibilities<ul><li>Design and manage scalable, secure, and highly available AWS infrastructure.</li><li>Build and maintain Kubernetes environments (EKS or self-managed).</li><li>Develop and optimize CI/CD pipelines for automated deployments.</li><li>Implement Infrastructure as Code using Terraform or CloudFormation.</li><li>Collaborate with engineering teams on cloud-native and microservices architectures.</li><li>Monitor system performance and ensure reliability through logging, metrics, and alerting.</li><li>Apply security best practices across infrastructure and deployment pipelines.</li><li>Participate in Agile development processes and technical discussions.</li><li>Create and maintain technical documentation.</li></ul>

DevOps Engineer (Tokyo, Japan)

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<ul><li style="text-align:justify;">Develop and maintain backend systems and APIs using Python Django.</li><li style="text-align:justify;">Build and support frontend applications using Angular.</li><li style="text-align:justify;">Design, develop, and optimize scalable, secure, and high-performance applications.</li><li style="text-align:justify;">Work with PostgreSQL for database design, integration, and optimization.</li><li style="text-align:justify;">Implement authentication and authorization solutions using OAuth2 and OIDC.</li><li style="text-align:justify;">Write clean, maintainable code and ensure software quality through testing and code reviews.</li><li style="text-align:justify;">Participate in system design discussions and contribute to technical solutions.</li><li style="text-align:justify;">Collaborate with cross-functional teams in an Agile environment.</li><li style="text-align:justify;">Support troubleshooting, performance improvements, and mentoring team members when needed.</li></ul>

Fullstack Developer (Python Django, Angular - English Fluent)

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Summary:&nbsp; The Data Scientist role focuses on building propensity models and sizing market opportunities for VMO project with banking clients. This hybrid position requires a blend of data science and engineering skills to create production-grade pipelines. The role is pivotal in driving sales strategy through data-driven outputs. Candidates should have a strong background in Python and SQL, along with proven experience in propensity modeling.<ol><li>Propensity Modeling &amp; Ranking: &nbsp; &nbsp; Predict CTR and engagement probability &nbsp; &nbsp; Use models like XGBoost / LightGBM &nbsp; &nbsp; Optimize ranking using NDCG, MRR, Precision@K</li><li>Feature Engineering: &nbsp; &nbsp; Build features from user behavior, interaction signals, and context &nbsp; &nbsp; Handle sparse and noisy data</li><li>Cold Start Handling: &nbsp; &nbsp; Design strategies for new users and new offers &nbsp; &nbsp; Implement hybrid and fallback approaches</li><li>Bias Handling: &nbsp; &nbsp; Mitigate popularity and exposure bias &nbsp; &nbsp; Implement diversity and re-ranking strategies</li><li>Model Evaluation: &nbsp; &nbsp; Define and track CTR, conversion, NDCG, AUC &nbsp; &nbsp; Continuously improve engagement metrics</li><li>Scalability &amp; Deployment: &nbsp; &nbsp; Build systems for millions of users &nbsp; &nbsp; Enable real-time or near real-time inference</li></ol>

AI Engineer ( Strong Python) - ENG GOOD

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Job Summary We are looking for 2 Senior Full-Stack Developers to join a cloud-native product team building enterprise-grade applications on Microsoft Azure. The role involves designing and implementing frontend interfaces, backend APIs, and microservices that integrate deeply with Azure cloud services. You will work across the full delivery lifecycle, from architecture decisions to CI/CD pipeline setup and production deployment.Key Responsibilities • &nbsp; &nbsp;Design and build responsive frontend applications using React or Angular, TypeScript, and chart libraries • &nbsp; &nbsp;Develop and maintain backend APIs and microservices using C# and .NET, following RESTful design principles • &nbsp; &nbsp;Implement background services, messaging queue patterns, and the Saga Pattern for distributed transaction management • &nbsp; &nbsp;Integrate applications with Azure services including Azure Front Door, API Management, Application Gateway, Azure Service Bus, Azure Redis, Azure Key Vault, and managed databases • &nbsp; &nbsp;Write and maintain CI/CD pipelines on Azure Pipelines, including build, test, scan, and deploy stages following DevSecOps practices • &nbsp; &nbsp;Apply security practices including SAST, dependency scanning, image scanning, secret management, and pipeline security gates • &nbsp; &nbsp;Containerize services using Docker with multi-stage builds; deploy and troubleshoot workloads on Azure Kubernetes Service (AKS) • &nbsp; &nbsp;Participate in Agile/Scrum ceremonies; understand and apply the 12 Cloud Native Factors in application design • &nbsp; &nbsp;Follow governance and security policies for Azure Resource Groups, service configurations, and user access control

Fullstack (.Net+ReactJS/Angular) DU33 - ENG GOOD

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<ul><li>GenAI developers to design, prototype, and deploy AI-driven solutions that optimize internal business processes.&nbsp;</li><li>The team will collaborate on a shared project under the guidance of a closely involved Technical Leader and work closely with the Product Owner on daily execution and prioritization.</li><li>Sample project scopes includes: - HR Agent – Build an AI assistant to respond to HR-related inquiries and support internal staff.&nbsp; - Power Automate Integration – Develop automated reporting workflows and streamline operational processes.&nbsp; - Legal Agent – Create an AI system capable of reviewing customer contracts and performing structured analysis for the legal team. &nbsp;</li></ul>

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