Drug discovery has always been a race against time.From identifying promising biological targets to designing molecules, validating candidates, and moving them toward clinical development, bringing a new drug to market can take more than a decade and require enormous investment. Even after years of research, many candidates fail because they are ineffective, unsafe, or simply impossible to develop at scale.Generative AI is beginning to change that equation.Unlike traditional AI systems that primarily analyze existing data, generative AI can&nbsp;create new molecules, predict their properties, generate hypotheses, and explore potential drug candidates that researchers may never have considered manually.The result is a new model for pharmaceutical R&amp;D: instead of searching through a vast chemical universe one possibility at a time, researchers can increasingly use AI to generate and prioritize the most promising paths.<h2>Why Drug Discovery Takes So Long</h2>Traditional drug discovery involves multiple stages, each with its own bottlenecks.Researchers first identify a biological target associated with a disease. They then search for compounds that can interact with that target, optimize those compounds for potency and selectivity, evaluate their safety and pharmacokinetic properties, and eventually move successful candidates into preclinical and clinical testing.A simplified process looks like this:Disease research → Target identification → Hit discovery → Lead optimization → Preclinical testing → Clinical trials → Regulatory approvalThe challenge is that each stage produces uncertainty.A molecule may bind effectively to a target but have poor absorption. Another may show strong activity in laboratory experiments but prove toxic in animals. A promising candidate may also be too difficult or expensive to manufacture.<h2>From Finding Molecules to Designing Them</h2>One of the biggest changes introduced by generative AI is the shift from&nbsp;molecule screening to molecule generation.Traditional virtual screening evaluates large libraries of existing compounds to identify molecules that could potentially interact with a biological target.Generative models approach the problem differently.They can learn patterns from existing chemical and biological data and then generate entirely new molecular structures designed around specific objectives.For example, researchers could ask an AI system to generate molecules that:<ul><li>Bind strongly to a specific protein target</li><li>Have improved selectivity</li><li>Reduce predicted toxicity</li><li>Have better solubility</li><li>Meet specific pharmacokinetic requirements</li><li>Remain synthetically feasible</li></ul>Instead of asking,&nbsp;“Which molecule in this library might work?”, researchers can increasingly ask:“What molecule should we design for this biological problem?”That is a fundamental shift in the drug discovery workflow.<h2>How Generative AI Is Accelerating R&amp;D</h2><h3>1. Faster Target and Disease Research</h3>Generative AI can process enormous amounts of scientific information, including research papers, genomic data, protein information, clinical data, and proprietary pharmaceutical datasets.This can help researchers identify relationships that might otherwise take significant time to uncover.AI can help answer questions such as:<ul><li>Which biological pathways are associated with a disease?</li><li>Which proteins could represent promising therapeutic targets?</li><li>What evidence supports a particular target?</li><li>Which mechanisms have already been explored?</li><li>Where are the gaps in existing research?</li></ul>Rather than replacing scientists, these systems can act as research copilots that help teams navigate increasingly complex scientific knowledge.<h3>2. De Novo Molecule Generation</h3>Perhaps the most transformative application is&nbsp;de novo drug design.Generative models can create new molecular structures rather than simply selecting from existing chemical libraries.Researchers can then computationally evaluate these candidates before deciding which ones deserve laboratory testing.This can dramatically reduce the number of molecules that need to be synthesized and tested physically.The workflow becomes:Generate → Predict → Rank → Synthesize → Test → RefineInstead of synthesizing thousands of possibilities and discovering which ones work, researchers can potentially focus laboratory resources on a much smaller set of AI-prioritized candidates.<h3>3. Protein Structure Prediction</h3>Understanding the three-dimensional structure of proteins is critical to modern drug discovery.Knowing how a protein folds can help researchers understand where and how a drug molecule might interact with it.Advances in AI-based protein structure prediction have significantly expanded researchers' ability to study biological targets whose structures were previously difficult or expensive to characterize.This creates an important connection between&nbsp;protein intelligence and generative drug design.AI can help researchers understand the target and then generate molecules designed to interact with it.<h3>4. Multi-Parameter Optimization</h3>Drug candidates rarely need to be optimized for only one characteristic.A successful molecule may need to be potent, selective, stable, soluble, safe, orally bioavailable, and manufacturable at a reasonable cost.Optimizing all these properties simultaneously is extremely difficult.Generative AI can help explore this multi-dimensional design space by generating candidates against multiple constraints.This allows drug developers to consider&nbsp;developability earlier, rather than discovering late in the process that a promising molecule has an unacceptable weakness.<h3>5. Smarter Preclinical Decisions</h3>AI can also help researchers predict how candidate molecules may behave before extensive laboratory testing.These predictions are not substitutes for experimental validation. However, they can help researchers identify weaker candidates earlier and prioritize experiments with greater potential value.That can reduce wasted laboratory time, materials, and resources.<h2>The Rise of AI-Native Drug Discovery</h2>Generative AI is also creating a new category of pharmaceutical companies:&nbsp;AI-native drug discovery companies.Rather than using AI as a supporting tool inside a traditional R&amp;D organization, these companies are building their discovery processes around computational models from the beginning.Their approach typically combines:Generative AI + Biological Data + Automation + High-Throughput ExperimentationThe goal is not simply to build better prediction models. It is to create a continuous feedback loop between computation and experimentation.<h2>Why Speed Matters So Much</h2>Reducing drug discovery timelines is not simply about getting products to market faster.Every year removed from R&amp;D can have significant economic value.Pharmaceutical companies invest heavily in research programs where the probability of failure is high. If AI can help identify ineffective candidates earlier, prioritize stronger candidates, and reduce unnecessary experiments, the impact can extend beyond development speed.It could potentially mean:Lower R&amp;D costs + Faster experimentation + Better candidate selection + More efficient use of scientific talentFor patients, the ultimate benefit could be even more important: faster access to new therapies for diseases where current treatment options remain limited.<h2>But Generative AI Is Not a Magic Shortcut</h2>Despite its potential, AI does not eliminate the fundamental challenges of drug development. A molecule that looks excellent computationally can still fail in the laboratory. A successful experiment in a cell model does not guarantee success in animals.The biggest misconception is that generative AI can simply “generate a drug.”In reality, AI can accelerate&nbsp;parts of the discovery and optimization process, but the path from an AI-generated molecule to an approved medicine still requires rigorous scientific validation.<h2>The Future: AI and Scientists Working as One System</h2>The future of drug discovery is unlikely to be humans versus AI. It will be humans&nbsp;with AI.Scientists bring domain expertise, biological intuition, experimental judgment, and the ability to interpret unexpected results. AI brings scale, computational power, pattern recognition, and the ability to explore enormous design spaces.Together, they can create a much faster R&amp;D feedback loop.The pharmaceutical organization of the future may look less like a linear pipeline and more like an interconnected system:Scientific knowledge → AI hypothesis → Generative design → Simulation → Automated experiment → Experimental data → AI learning → New hypothesisThis creates the possibility of continuously improving discovery cycles rather than moving through isolated research stages.<h2>What This Means for Pharma Companies</h2>For pharmaceutical leaders, the question is no longer simply whether generative AI will influence drug discovery.The more strategic question is&nbsp;where AI can create measurable value across the R&amp;D lifecycle.The most successful implementations will likely combine AI capabilities with strong data foundations, domain expertise, cloud infrastructure, and secure engineering environments.<h2>The New Drug Discovery Race</h2>Generative AI is changing the competitive landscape of pharmaceutical R&amp;D.The companies that gain an advantage may not simply be those with the largest research budgets or the biggest compound libraries. Increasingly, the advantage could belong to organizations that can&nbsp;turn data into hypotheses, hypotheses into experiments, and experiments into validated candidates faster than their competitors.And as AI models become more capable, laboratory automation becomes more connected, and biological datasets continue to expand, the traditional decade-plus drug discovery journey may increasingly become a race measured not only in years, but in how intelligently every experiment is chosen.

The New Drug Discovery Race: How Generative AI Is Cutting Years Off R&D
Drug discovery has always been a race against time.
From identifying promising biological targets to designing molecules, validating candidates, and moving them toward clinical development, bringing a new drug to market can take more than a decade and require enormous investment. Even after years of research, many candidates fail because they are ineffective, unsafe, or simply impossible to develop at scale.

Generative AI: Speeding Up Drug Discovery R&D

The New Drug Discovery Race: How Generative AI Is Cutting Years Off R&D

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Celebrating 14 years of growth, connection, and the people who have shaped our journey, VMO proudly marks another milestone in our story - one built by thousands of people, countless moments, and a shared belief in moving forward together.This year, we brought that spirit to Ha Long for a special celebration, from shared moments during our vacation to the highlight of the celebration - “Ra Khơi” Gala Night, where we came together to celebrate VMO’s 14th birthday and honor our colleagues in recognition of their seniority and dedication.<figure class="image"><img src="https://vmo-ho-web-global.s3.ap-southeast-1.amazonaws.com/vmogroup-global/uploads/1786508283429-185719HZ8_5872+copy.jpg"></figure>14 years is more than a milestone. It is a journey shaped by people, strengthened by challenges, and built on the belief that we can always move forward together.Thank you to everyone who has been part of this journey - for your dedication, energy, and commitment that continue to shape the VMO story.As we celebrate where we have been, we look forward to everything that lies ahead - carrying our heritage forward, together.Happy 14th Anniversary, VMO! Here’s to the next chapter.&nbsp;<figure class="image"><img src="https://vmo-ho-web-global.s3.ap-southeast-1.amazonaws.com/vmogroup-global/uploads/1786508386624-181855-HAI_8417.JPG"></figure>

Celebrating 14 years of growth, connection, and the people who have shaped our journey, VMO proudly marks another milestone in our story - one built by thousands of people, countless moments, and a shared belief in moving forward together.

VMO14 ANNIVERSARY | THE HERITAGE

Celebrating 14 years of growth, connection, and the people who have shaped our journey, VMO proudly marks another milestone in our story

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Clinical documentation has always been an essential part of healthcare, but it is also one of the biggest administrative burdens for clinicians. Physicians can spend hours typing notes, updating EHRs, and completing documentation after patient consultations.Ambient AI is changing this workflow.Instead of asking doctors to document every detail manually, ambient AI can listen to clinical conversations, understand the context, and automatically generate structured medical notes. The physician simply reviews and approves the output.<h2>From Listening to Documentation</h2>Ambient AI combines speech recognition, natural language processing, and generative AI to turn conversations into clinical documentation.During a consultation, the system can identify symptoms, medical history, diagnoses, treatment discussions, and follow-up plans. It then organizes this information into a clinical note that can be reviewed before being added to the EHR.The shift is simple:AI drafts. Clinicians validate.This allows physicians to spend less time looking at screens and more time engaging with patients.<h2>More Than Just Transcription</h2>Ambient AI is evolving beyond automated transcription. Once AI understands a clinical conversation, it can potentially support other parts of the healthcare workflow.The same information could help generate patient summaries, support medical coding, trigger referrals, or assist with care coordination. In this sense, ambient AI could become more than a documentation assistant, it could become a foundation for more automated clinical workflows.<h2>Integration and Trust Are Critical</h2>For hospitals, implementing ambient AI is not simply about choosing an AI model. The technology needs to work securely with existing EHRs and healthcare systems while meeting strict requirements for privacy, compliance, and data governance.Accuracy is equally important. AI-generated notes still require clinical review because even a small error can have significant consequences.The ideal model is therefore:AI-generated → Clinician-reviewed → Clinician-approved<h2>The Future: AI That Disappears Into the Workflow</h2>The most interesting thing about ambient AI may be that clinicians barely notice it.Instead of introducing another application or screen, AI works quietly in the background turning conversations into documentation and documentation into actionable workflows.As the technology matures, clinical documentation may become less of a task clinicians perform and more of a process that happens automatically.The future of healthcare AI may not be about adding more technology to clinical workflows. It may be about making technology increasingly invisible.

The Rise of Ambient AI: How Clinical Documentation Is Disappearing

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Artificial intelligence has become a familiar part of healthcare, with chatbots helping patients book appointments, answer common questions, and provide basic support. While these tools have improved accessibility, they represent only the first wave of AI adoption.Today, healthcare is entering a new era powered by&nbsp;AI agents intelligent systems that do far more than respond to prompts. They can understand objectives, make decisions, coordinate across multiple applications, and execute entire workflows with minimal human intervention. For healthcare organizations facing workforce shortages, rising operational costs, and growing patient expectations, AI agents have the potential to transform how care is delivered and managed.<h2>From Conversation to Action</h2>Traditional AI chatbots are designed to interact with users through conversation. They answer questions, retrieve information, or guide patients through simple processes. While useful, they rely on users to initiate every interaction and are limited to providing information rather than completing tasks.AI agents take a significant step forward. Instead of simply answering a question, they can manage an entire process from start to finish. An AI agent can retrieve patient information, verify insurance coverage, schedule appointments, update electronic health records, notify care teams, and monitor the outcome all within a single workflow.Rather than acting as another software tool, AI agents function more like digital teammates that collaborate with healthcare professionals.<h2>Why Healthcare Needs AI Agents</h2>Healthcare systems generate enormous amounts of data and involve countless administrative processes that often require manual effort. Clinicians and staff spend valuable time documenting patient encounters, coordinating appointments, processing insurance claims, and managing follow-up care.These repetitive tasks not only increase operational costs but also contribute to clinician burnout and longer waiting times for patients.AI agents help reduce this burden by automating routine processes while allowing healthcare professionals to focus on clinical decision-making and patient care. Instead of replacing human expertise, they augment healthcare teams by handling repetitive work with greater speed and consistency.<h3>Real-World Applications</h3>AI agents are already delivering measurable value across a wide range of healthcare operations. Some of the most impactful use cases include:<ul><li>Intelligent Patient Scheduling – Matches patients with the right specialists, verifies insurance eligibility, optimizes appointment availability, sends reminders, and automatically manages cancellations or rescheduling.</li><li>Clinical Documentation – Captures conversations, generates structured clinical notes, updates electronic health records (EHRs), and supports medical coding, allowing clinicians to spend more time with patients.</li><li>Care Coordination – Tracks treatment plans, monitors follow-up appointments and lab results, sends medication reminders, and alerts care teams when timely intervention is needed.</li><li>Revenue Cycle Management – Automates insurance verification, prior authorization, claims validation, billing workflows, and reimbursement tracking to improve efficiency and reduce administrative errors.</li><li>Population Health Management – Analyzes patient data to identify high-risk individuals, detect gaps in care, recommend preventive interventions, and support proactive chronic disease management.</li><li>Patient Engagement – Delivers personalized reminders, answers routine inquiries, provides post-discharge guidance, and proactively checks in with patients to improve adherence and overall care experiences.</li></ul><h2>Building Enterprise AI Workflows</h2>The intelligence of an AI agent comes from combining several technologies into a coordinated system. Large language models provide natural language understanding, while workflow orchestration enables the agent to execute multiple tasks across different enterprise applications. Integration with electronic health records, scheduling platforms, billing systems, and clinical databases allows AI agents to move seamlessly between systems rather than operating in isolation.Equally important is human oversight. Healthcare decisions require transparency, accountability, and compliance with strict regulatory standards. AI agents should support clinicians by providing recommendations and automating administrative work, while medical professionals remain responsible for patient care and final clinical decisions.<h2>Getting Started</h2>Many healthcare organizations believe AI transformation requires replacing existing technology platforms. In reality, the most successful initiatives often begin with a single workflow that delivers measurable value.Starting with processes such as appointment scheduling, clinical documentation, or claims processing allows organizations to demonstrate return on investment before expanding AI capabilities across other departments. This phased approach reduces implementation risk while creating a strong foundation for broader digital transformation.<h2>The Future of Healthcare Operations</h2>Healthcare is moving beyond AI that simply answers questions. The next generation of intelligent systems will actively coordinate workflows, automate routine operations, and assist healthcare professionals throughout the patient journey.As AI agents continue to mature, they will help hospitals and healthcare providers improve efficiency, reduce administrative costs, deliver more personalized patient experiences, and enable clinicians to spend more time where they create the greatest value—caring for patients.<h2>How VMO Supports Healthcare Innovation</h2>At VMO, we help healthcare providers, life sciences organizations, and healthtech companies design and implement enterprise AI solutions that deliver measurable business outcomes.From AI agent development and workflow automation to healthcare software engineering, cloud modernization, data engineering, and system integration, our teams build secure, scalable solutions that fit seamlessly into existing healthcare ecosystems.Whether your goal is to automate administrative processes, enhance patient engagement, or build intelligent healthcare platforms, VMO combines healthcare expertise with advanced AI capabilities to accelerate your digital transformation journey.<h2>Conclusion</h2>AI agents represent the next stage of healthcare innovation. Unlike traditional chatbots, they can coordinate complex processes, connect multiple systems, and execute end-to-end workflows while keeping healthcare professionals in control.Organizations that embrace AI agents today will be better positioned to improve operational efficiency, enhance patient experiences, and build more resilient healthcare systems for the future.

AI Agents in Healthcare: Beyond Chatbots to Autonomous Workflows

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Healthcare has always embraced technologies that improve patient outcomes, from robotic surgery and AI-assisted diagnostics to virtual care. Now, another innovation is entering the conversation: the&nbsp;metaverse.While public interest in the metaverse has cooled compared to its peak in 2022, healthcare organizations are quietly finding practical ways to leverage immersive virtual environments, particularly in&nbsp;medical education and clinical training.The question is no longer whether the metaverse will replace hospitals. It's whether it can become the next generation of healthcare training.<h2>Beyond the Buzzword: What Does the Metaverse Mean for Healthcare?</h2>In healthcare, the metaverse isn't about virtual real estate or digital avatars attending meetings.Instead, it refers to immersive, interconnected virtual environments powered by technologies such as:<ul><li>Virtual Reality (VR)</li><li>Augmented Reality (AR)</li><li>Mixed Reality (MR)</li><li>AI-powered digital patients</li><li>Digital twins</li><li>Real-time collaboration platforms</li></ul>Together, these technologies allow medical professionals to practice, collaborate, and learn in realistic clinical scenarios without putting real patients at risk.The focus shifts from entertainment to&nbsp;simulation, collaboration, and continuous learning.<h2>Why Traditional Medical Training Has Its Limits</h2>Medical education has always relied on a combination of classroom instruction, cadaver labs, simulations, and supervised clinical practice. While effective, these approaches face several challenges:<ul><li>Limited access to rare or complex clinical cases</li><li>High costs for simulation centers and physical equipment</li><li>Geographic barriers to expert instruction</li><li>Difficulty repeating identical scenarios for consistent assessment</li><li>Limited opportunities for multidisciplinary collaboration</li></ul>As healthcare becomes more complex, professionals need training environments that are scalable, repeatable, and increasingly realistic.<h2>How the Metaverse Is Transforming Medical Training</h2><h3>1. Risk-Free Surgical Simulation</h3>Surgeons can practice intricate procedures repeatedly within highly immersive virtual operating rooms.Unlike traditional simulation labs, virtual environments allow:<ul><li>Unlimited practice sessions</li><li>Performance tracking</li><li>AI-driven feedback</li><li>Rare-case simulations</li><li>Team-based procedure rehearsals</li></ul>This accelerates skill development while reducing dependence on expensive physical resources.<h3>2. AI-Powered Virtual Patients</h3>Instead of standardized patients or scripted mannequins, AI-powered virtual patients can:<ul><li>Respond dynamically to treatments</li><li>Display changing vital signs</li><li>Simulate multiple disease progressions</li><li>Challenge diagnostic reasoning</li><li>Provide communication practice</li></ul>Medical students can experience thousands of patient interactions before entering clinical practice.<h3>3. Emergency Response Training</h3>Emergency medicine depends on speed, coordination, and communication.Virtual hospitals enable multidisciplinary teams to rehearse:<ul><li>Trauma resuscitation</li><li>Cardiac arrest</li><li>Stroke response</li><li>Mass casualty events</li><li>Pandemic preparedness</li></ul>These scenarios can be repeated under varying conditions, helping teams improve decision-making and teamwork.<h3>4. Remote Learning Without Geographic Barriers</h3>Healthcare expertise is unevenly distributed across the world. Immersive virtual classrooms allow specialists to mentor clinicians regardless of location.A surgeon in Singapore could teach residents in Vietnam, Australia, or Indonesia inside the same virtual operating room, observing their techniques from multiple perspectives in real time. This expands access to world-class medical education without requiring international travel.<h3>5. Digital Twins for Personalized Learning</h3>Digital twins virtual replicas of patients, organs, or healthcare systems, are becoming an exciting frontier.Rather than practicing on generalized anatomy, clinicians may eventually rehearse procedures using patient-specific anatomical models built from CT or MRI scans.This could improve surgical planning while giving trainees experience with highly individualized cases.<h2>The Benefits Extend Beyond Training</h2>The metaverse may also reshape broader healthcare operations.Potential applications include:<ul><li>Virtual multidisciplinary care meetings</li><li>Remote specialist consultations</li><li>Hospital workflow simulations</li><li>Medical device demonstrations</li><li>Continuing professional education</li><li>Patient education and rehabilitation</li></ul>As immersive technologies mature, hospitals may use virtual environments to improve collaboration across departments and even across countries.<h2>But the Challenges Are Real</h2><h3>Technology Costs</h3>High-quality VR headsets, motion tracking systems, and immersive software platforms require significant investment. Although costs continue to decline, widespread deployment remains expensive for many healthcare organizations.<h3>Data Privacy and Security</h3>Virtual healthcare environments often involve sensitive clinical information. Protecting patient data while complying with healthcare regulations remains essential.<h3>Integration with Existing Systems</h3>Immersive platforms must integrate with electronic health records (EHRs), imaging systems, and hospital IT infrastructure to deliver meaningful clinical value.<h3>Clinical Validation</h3>Not every virtual simulation produces better learning outcomes.Healthcare organizations increasingly demand evidence showing that immersive training improves clinician performance and ultimately benefits patients.Technology alone is not enough, it must demonstrate measurable educational impact.<h2>Is the Metaverse Just Hype?</h2>The excitement surrounding the broader metaverse has undoubtedly faded.However, healthcare appears to be following a different trajectory.Instead of pursuing fully virtual hospitals, organizations are adopting targeted immersive technologies where they provide clear value. Medical simulation has existed for decades.Rather than replacing traditional education, immersive technologies are becoming powerful complements to hands-on clinical training.What Healthcare Leaders Should ConsiderHealthcare organizations exploring immersive training should focus on business outcomes rather than technology trends.Key questions include:<ul><li>Which clinical skills would benefit most from immersive simulation?</li><li>Can virtual training improve patient safety?</li><li>How can AI personalize learning experiences?</li><li>What infrastructure is needed for secure deployment?</li><li>How will success be measured?</li></ul>Starting with targeted pilot programs often delivers greater value than attempting large-scale virtual transformation initiatives.<h2>The Road Ahead</h2>The future of medical training is unlikely to exist entirely in physical classrooms—or entirely in virtual worlds.Instead, healthcare education is moving toward a hybrid model that combines real-world clinical experience with immersive digital environments powered by AI, simulation, and extended reality technologies.Hospitals may never exist entirely in the metaverse.But the next generation of doctors, nurses, surgeons, and healthcare professionals will almost certainly spend part of their education there.For healthcare organizations facing workforce shortages, increasing clinical complexity, and rising expectations for patient safety, immersive training is becoming less of a futuristic concept and more of a strategic investment.

Hospitals in the Metaverse: Hype or the Future of Medical Training?

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Metal 3D printing is transforming the way high-precision components are manufactured. Yet ensuring quality throughout a layer-by-layer production process remains one of the industry's most complex technical challenges. Detecting defects early is critical, not only for maintaining product quality, but also for reducing material waste, production time, and operational costs.

AI benchTALKs #04 | AI for Advanced Metal 3D Printing

AI benchTALKs #04 | AI for Advanced Metal 3D Printing: How AI Is Reshaping Precision Manufacturing

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As a Baseband PHY Software Engineer, you will lead the design and development of high-performance, multi-core system software for LTE/5G baseband systems. You will play a key role in architecture decisions, performance optimization, and technical mentorship.

Junior Baseband PHY Software Design Engineers - ENGLISH REQUIRED

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As a Baseband PHY Software Engineer, you will lead the design and development of high-performance, multi-core system software for LTE/5G baseband systems. You will play a key role in architecture decisions, performance optimization, and technical mentorship.

Fresher Baseband PHY Software Design Engineers - English required

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Responsible for building and maintaining CI/CD pipelines (GitHub Actions), automating tasks via Bash/Python, and containerizing applications on Docker/Kubernetes. Monitor infrastructure using Dynatrace, Grafana, and ELK. Provide production support, incident resolution, data patching, and SLA-bound ticket management via ServiceNow to ensure high system availability.Key Responsibilities<ul><li>CI/CD &amp; Automation: Build, maintain, and troubleshoot CI/CD pipelines, and develop modules using GitHub Actions. Identify manual tasks and automate them using Bash or Python scripts. &nbsp;</li><li>Infrastructure &amp; Containerization: Containerize legacy applications, build custom containers according to compliance, and monitor infrastructure using tools like Dynatrace. &nbsp;</li><li>Production Support: Participate in support activities including data patching, monitoring, incident resolution, and implementing permanent fixes. Ensure high application availability and minimize service disruptions. &nbsp;</li><li>Service Management: Respond to user enquiries and service requests, resolving tickets within agreed internal SLAs. Manage tool upgrades, support POCs, and proactively maintain support knowledge documentation.</li></ul>

DevOps Engineer - AZURE - ENG Fluent

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About VMOVMO - a proud member of the ALTEN Group, a global leader in engineering and IT services with over 60,000 employees worldwide. At VMO, we partner with clients across Asia, the Americas, and Europe, delivering software development and digital transformation solutions that drive real impact on businesses and society. From day one, our vision has been to build more than just a technology company – we aim to create a place where forward thinkers can thrive, innovate, and make a difference. At VMO, local talent connects with global opportunities. We are home to innovators, dreamers, and doers - and we are always looking for passionate people to join us and shape the future together.&nbsp;Job Descriptions<ul><li>Analyze and document business requirements, create BRD, functional specifications, and user stories</li><li>Facilitate stakeholder workshops and requirement gathering sessions</li><li>Collaborate with cross-functional teams to identify process improvements</li><li>Participate in Scrum ceremonies and support Agile development processes</li><li>Conduct gap analyses and feasibility studies for proposed changes</li><li>Support testing activities, including UAT and system integration testing</li><li>Perform data analysis to provide business insights and recommendations</li><li>Participate in bidding process</li></ul>

Middle - Senior Business Analyst [Fintech/ Healthcare Domain - Offer 45M]

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1. Bid Management &amp; Coordination<ul><li>Primary Point of Contact for Bid Initiation: Receive bid information, participate in, and coordinate activities related to bid initiation in accordance with project requirements and company procedures.</li><li>Bid Tracking &amp; Status Updates: Monitor and update the progress of bids from issuance through final results. Coordinate with relevant departments and key stakeholders to ensure timely, accurate, and complete information is maintained throughout the bid process.</li></ul>2. Internal Operations Support for the Production Division<ul><li>Support internal operational activities, including monitoring production KPIs and assisting affiliated units with documentation and administrative processes as needed, ensuring compliance with company policies, procedures, and regulations.</li></ul>3. Documentation &amp; Reporting<ul><li>Periodic Reporting: Consolidate data and prepare regular reports to support operational tracking and management decision-making.</li></ul>

OPC Executive

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<ul><li>Develop responsive user interfaces and robust backend microservices.</li><li>Design and maintain RESTful APIs and enterprise system integrations.</li><li>Support application modernization, security remediation, and production releases.</li><li>Collaborate with cross-functional Agile teams (Product Managers, Architects, BAs, QA, DevOps).</li></ul>

Java Developer - ENGLISH GOOD - DU33

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<ul><li>Design, develop, and maintain ETL/ELT pipelines to ingest, transform, and load data from multiple sources into cloud data platforms.</li><li>Build and optimize data models following Medallion architecture (Bronze/Silver/Gold layers) or equivalent layered data design.</li><li>Develop and maintain data pipelines using tools such as Databricks, Azure Data Factory, or Microsoft Fabric.</li><li>Ensure data quality, consistency, and reliability through validation, testing, and monitoring frameworks.</li><li>Collaborate with Data Analysts, Data Scientists, and Backend Engineers to define data requirements and deliver fit-for-purpose datasets.</li><li>Optimize query performance and storage costs across data warehouse and lakehouse environments.</li><li>Implement and maintain CI/CD pipelines for data workflows, including version control and automated deployment.</li><li>Apply data governance, security, and access control best practices in line with project and compliance requirements.</li><li>Troubleshoot data pipeline issues and provide timely resolution to minimize downtime and business impact.</li><li>Document data architecture, pipeline logic, and technical processes for team knowledge sharing.</li><li>Participate in technical discussions and provide input on data architecture decisions for ongoing and upcoming projects.</li></ul>

DATA ENGINEER _ ENG GOOD _ DU33

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<ul><li>Design, develop, and maintain automated test suites using Playwright and TypeScript.</li><li>Build and maintain automation test cases for end-to-end, integration, regression, and release validation.</li><li>Validate frontend functionality, business logic, user workflows, API interactions, and system integrations.</li><li>Perform both white-box testing and black-box testing for modern web applications.</li><li>Read and analyze frontend source code to identify potential issues, quality risks, and root causes.</li><li>Review application architecture and code changes to assess possible impact on product quality.</li><li>Investigate defects across frontend, backend, API, and integration layers.</li><li>Validate frontend-backend communication, authentication/authorization flows, and asynchronous workflows.</li><li>Collaborate closely with Frontend Engineers, Backend Engineers, Product Managers, and Designers.</li><li>Participate in UAT, release testing, and production readiness validation.</li><li>Contribute to test strategy, QA process improvement, automation best practices, and continuous quality improvement.</li><li>Support or contribute to AI/agentic QA systems or testing loops when applicable.</li></ul>

Automation Tester - ENG GOOD - DU33

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<ul><li>Own the product roadmap and define the strategic direction for product development.</li><li>Create and manage Product Requirement Documents (PRDs), user stories, acceptance criteria, and product specifications.</li><li>Translate business and regulatory requirements into clear, actionable product requirements for the Engineering team.</li><li>Collaborate closely with Engineering and Solution Architects to deliver new product features.</li><li>Lead the development of governance and compliance-related features, including:<ul><li>Retention Policies</li><li>Records Classification</li><li>e-Discovery / Legal Hold</li><li>Regulatory Reporting</li></ul></li><li>Define product requirements for integrations with enterprise systems such as IAM/SSO, DLP, SIEM, E-signature solutions, and Document Management Systems (DMS).</li><li>Ensure product consistency and functionality across SaaS, On-Premises, and OEM/White-label deployment models.</li></ul>

Product Manager - ENGLISH GOOD - DU33

product-manager-english-good-du33

<ul><li>&nbsp;Design, develop, and maintain ETL/ELT pipelines to ingest, transform, and load data from multiple sources into cloud data platforms.</li><li>Build and optimize data models following Medallion architecture (Bronze/Silver/Gold layers) or equivalent layered data design.</li><li>Develop and maintain data pipelines using tools such as Databricks, Azure Data Factory, or Microsoft Fabric.</li><li>Ensure data quality, consistency, and reliability through validation, testing, and monitoring frameworks.</li><li>Collaborate with Data Analysts, Data Scientists, and Backend Engineers to define data requirements and deliver fit-for-purpose datasets.</li><li>Optimize query performance and storage costs across data warehouse and lakehouse environments.</li><li>Implement and maintain CI/CD pipelines for data workflows, including version control and automated deployment.</li><li>Apply data governance, security, and access control best practices in line with project and compliance requirements.</li><li>Troubleshoot data pipeline issues and provide timely resolution to minimize downtime and business impact.</li><li>Document data architecture, pipeline logic, and technical processes for team knowledge sharing.</li><li>Participate in technical discussions and provide input on data architecture decisions for ongoing and upcoming projects.</li></ul>

Junior Baseband PHY Software Design Engineers - ENGLISH REQUIRED

Junior Baseband PHY Software Design Engineers - ENGLISH REQUIRED

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