Healthcare has always been a data-rich industry. From electronic health records to imaging scans, lab results, and real-world patient data, clinicians are surrounded by information. Yet, for decades, the challenge has not been data availability; it has been&nbsp;data usability.Today, artificial intelligence (AI) is closing that gap. AI is not just adding another layer of technology. AI is fundamentally transforming the speed, precision, and personalization of clinical decision-making.<h2>The Problem: Too Much Data, Not Enough Insight</h2>Modern clinicians face a paradox:<ul><li>More patient data than ever before</li><li>Less time to interpret it effectively</li></ul>A single patient journey may include:<ul><li>Thousands of medical records</li><li>Multiple imaging modalities</li><li>Continuous monitoring data</li><li>Genomic and lifestyle inputs</li></ul><h2>AI as the Clinical Co-Pilot</h2>Rather than replacing clinicians, AI is emerging as a&nbsp;decision support system — a co-pilot that enhances human judgment.<h3>1. Turning Raw Data into Actionable Insights</h3>AI models can aggregate and analyze diverse datasets:<ul><li>Structured data (lab results, vitals)</li><li>Unstructured data (clinical notes, discharge summaries)</li><li>Medical imaging (X-rays, MRIs, CT scans)</li></ul>By doing so, AI can:<ul><li>Detect patterns invisible to the human eye</li><li>Highlight anomalies early</li><li>Suggest potential diagnoses</li></ul><h3>AI in Radiology - Earlier Detection, Higher Confidence</h3>A study published in&nbsp;Nature showed that AI models for breast cancer screening could:<ul><li>Reduce false positives</li><li>Reduce false negatives</li><li>Improve overall diagnostic accuracy compared to traditional readings</li></ul>In practice, hospitals are using AI tools to:<ul><li>Pre-screen mammograms</li><li>Prioritize high-risk cases in radiology workflows</li><li>Support radiologists with second-opinion insights</li></ul>This does not replace radiologists,&nbsp; it&nbsp;augments them, allowing more focus on complex cases while routine scans are triaged intelligently.<h3>2. Enhancing Diagnostic Accuracy</h3>AI-powered clinical decision support systems (CDSS) can:<ul><li>Cross-reference symptoms with vast medical knowledge bases</li><li>Compare patient data against millions of historical cases</li><li>Provide probability-based diagnostic suggestions</li></ul><h3>Case Study: Sepsis Prediction in Critical Care</h3>Sepsis is one of the leading causes of mortality in hospitals, and early detection is critical.Hospitals using AI-driven early warning systems have been able to:<ul><li>Identify sepsis hours before clinical symptoms fully manifest</li><li>Trigger early interventions</li><li>Reduce mortality rates and ICU stays</li></ul>These systems analyze:<ul><li>Vital signs trends</li><li>Lab results</li><li>Patient history</li></ul>And continuously update risk scores in real time, something impossible to do manually at scale.<h3>3. Enabling Real-Time Decision-Making</h3>AI can process streaming data from:<ul><li>ICU monitors</li><li>Wearables</li><li>Remote patient monitoring systems</li></ul>And deliver:<ul><li>Early warnings</li><li>Risk scores</li><li>Treatment recommendations</li></ul>This shifts healthcare from reactive to&nbsp;proactive.<h2>Personalization at Scale</h2>Instead of “one-size-fits-all” treatment plans, AI can:<ul><li>Analyze genetic profiles</li><li>Incorporate lifestyle and behavioral data</li><li>Predict individual responses to treatments</li></ul><h3>AI in Oncology - Matching Patients to the Right Therapy</h3>In oncology, treatment decisions are highly complex.AI systems are now being used to:<ul><li>Analyze tumor genomics</li><li>Match patients with targeted therapies</li><li>Recommend clinical trials</li></ul>For example, leading cancer centers use AI platforms to scan thousands of research papers and clinical guidelines, helping oncologists make evidence-based decisions tailored to each patient.The result:<ul><li>More precise treatments</li><li>Reduced trial-and-error</li><li>Improved patient outcomes</li></ul><h2>Bridging the Gap Between Data and Workflow</h2>A key challenge in healthcare technology adoption is workflow disruption.Successful AI solutions do not operate in isolation. They integrate seamlessly into existing systems such as:<ul><li>Electronic Health Records (EHR)</li><li>Hospital Information Systems (HIS)</li><li>Clinical dashboards</li></ul>The goal is simple:Deliver the right insight, at the right time, within the clinician’s natural workflow.This is where engineering matters as much as algorithms.Building AI is one part of the equation. Making it usable in real clinical environments, reliable, fast, and interoperable, is what ultimately drives the impact.<h2>From Models to Systems: Making AI Work in Practice</h2>Many healthcare organizations have experimented with AI models. Fewer have successfully scaled them.The difference often lies in:<ul><li>Data pipelines that ensure high-quality, real-time inputs</li><li>Integration layers connecting AI with clinical systems</li><li>Governance frameworks to monitor performance and compliance</li><li>Continuous learning loops to improve models over time</li></ul>In other words, AI in healthcare is not just about intelligence, it is about&nbsp;system design.<h2>Trust, Transparency, and Human Oversight</h2>Despite its potential, AI in clinical decision-making comes with critical considerations:<ul><li>Explainability: Clinicians need to understand&nbsp;why a recommendation is made</li><li>Data quality: AI is only as good as the data it learns from</li><li>Bias and fairness: Models must be validated across diverse populations</li><li>Regulatory compliance: Ensuring safety and accountability</li></ul>ConclusionBehind every dataset is a patient. Behind every prediction is a decision that carries real consequences. AI brings speed, scale, and pattern recognition. Clinicians bring context, experience, and judgment.Healthcare's future will depend on how well AI and clinicians work together. When that balance is right, clinical decision-making becomes not just smarter but more human. &nbsp;

From Data to Diagnosis: How AI is Reshaping Clinical Decision-Making

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The Rising Wave - Year-End Party 2025: A Night of Convergence The music has faded, but the energy from The Rising Wave continues to resonate. Last week, we witnessed individual ripples of passion converge into a single, unstoppable force - a vibrant display of the "can-do" spirit and synergy that define us. From energetic stage performances to heartfelt connections, it was a night where our collective momentum marked a bold new chapter for ALTEN VMO. Beyond the excitement, we paused for shared reflection to honor our 2025 milestones and the incredible people whose dedication turns every challenge into a surge of success.&nbsp; More than a Year-End Party, it was a celebration of how far we’ve come and a preview of where we’re headed. To our team, partners, and global clients: thank you for powering this current. Let’s turn this shared momentum into a breakthrough in 2026! 🚀#ALTEN #TomorrowsWorld #MakeItHappen #TeamALTEN #ALTENVietnam #TheRisingWave #YearEndParty2025 #TogetherWeRise #Recap

The Rising Wave - Year-End Party 2025: A Night of Convergence

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The Tet atmosphere has officially filled every corner of VMO as we celebrated our New Year’s Kick-off ceremony - Khởi Chương.&nbsp;Across our offices in Vietnam and around the world, the spirit of the Lunar New Year served as a powerful bridge, uniting our global team under one shared vision. This celebration was more than just a tradition; it was a testament to our culture of appreciation and solidarity, ensuring every VMOer felt the warmth of home and the spark of new ambition.&nbsp;By honoring our roots while looking toward a breakthrough year, we’ve set a foundation of joy and determination that will drive us to conquer new heights together. Wishing the entire VMO a prosperous, energetic, and successful Year of the Horse!&nbsp;#ALTEN #ALTENAsia #ALTENVietnam #VMO #TomorrowsWorld #MakeItHappen #Tet #LunarNewYear

The Tet atmosphere has officially filled every corner of VMO as we celebrated our New Year’s Kick-off ceremony - Khởi Chương.

New Year’s Kick-off ceremony - Khởi Chương

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<h2>Why Life Sciences Companies Can’t Ignore AI Integration</h2>The life sciences industry is at a turning point.From drug discovery to patient care, organizations are under increasing pressure to move faster, reduce costs, and deliver more personalized outcomes. Traditional approaches, often reliant on manual processes and siloed data, are no longer sufficient.AI is no longer a “nice-to-have.” It’s becoming a core capability that separates industry leaders from those struggling to keep up.<h2>The Growing Complexity of Life Sciences</h2>Life sciences companies today face multiple challenges:<ul><li>Massive volumes of unstructured data (clinical, genomic, real-world data)</li><li>Increasing regulatory pressure</li><li>Rising R&amp;D costs with declining success rates</li><li>Demand for faster time-to-market</li><li>Need for personalized medicine</li></ul>These challenges are interconnected and difficult to solve using conventional systems alone.AI changes that equation.<h2>Where AI Creates Immediate Impact</h2><h3>1. Accelerating Drug Discovery</h3>AI can analyze millions of chemical compounds, predict molecular behavior, and identify promising drug candidates in a fraction of the time.What used to take years can now be reduced to months, significantly lowering early-stage R&amp;D costs and improving success rates.<h3>2. Enhancing Clinical Trials</h3>Clinical trials are often delayed due to patient recruitment issues, protocol inefficiencies, and data inconsistencies.AI helps by:<ul><li>Identifying suitable patient cohorts faster</li><li>Predicting trial risks and outcomes</li><li>Monitoring real-time data for early intervention</li></ul>The result: faster, more efficient trials with higher success probability.<h3>3. Unlocking the Power of Real-World Data</h3>Life sciences companies sit on vast amounts of data—from EHRs to wearable devices.AI enables:<ul><li>Data integration across fragmented systems</li><li>Advanced analytics for actionable insights</li><li>Better understanding of patient journeys</li></ul>This is critical for both regulatory submissions and post-market surveillance.<h3>4. Driving Personalized Medicine</h3>AI makes it possible to move from “one-size-fits-all” treatments to precision medicine.By analyzing genetic, clinical, and lifestyle data, AI supports:<ul><li>Tailored treatment plans</li><li>Improved patient outcomes</li><li>Reduced adverse effects</li></ul>This is not just innovation, it’s becoming an expectation.<h3>5. Optimizing Operations and Supply Chains</h3>Beyond R&amp;D, AI improves operational efficiency by:<ul><li>Forecasting demand</li><li>Detecting anomalies in manufacturing</li><li>Optimizing inventory and distribution</li></ul>This ensures resilience in an increasingly volatile global environment.<h2>Why Many Companies Still Struggle</h2>Despite clear benefits, many life sciences organizations face barriers to AI adoption:<ul><li>Legacy systems that limit data accessibility</li><li>Lack of an integrated data infrastructure</li><li>Shortage of AI and data engineering talent</li><li>Concerns around compliance and data security</li><li>Unclear ROI from pilot projects</li></ul>AI is not just a technology shift; it’s an organizational transformation.<h2>The Cost of Inaction</h2>Companies that delay AI integration risk:<ul><li>Falling behind competitors in innovation</li><li>Longer development cycles</li><li>Higher operational costs</li><li>Reduced ability to meet regulatory and patient expectations</li></ul>In a highly competitive and regulated industry, this gap widens quickly.<h2>How to Approach AI Integration Strategically</h2>Successful organizations don’t start with technology—they start with business value.A practical approach includes:<h3>1. Identify High-Impact Use Cases</h3>Focus on areas where AI can deliver measurable outcomes (e.g., trial optimization, data integration).<h3>2. Build a Scalable Data Foundation</h3>Ensure data is accessible, clean, and interoperable across systems.<h3>3. Start Small, Scale Fast</h3>Pilot quickly, validate ROI, then expand across the organization.<h3>4. Partner with the Right Technology Experts</h3>AI integration requires deep expertise in both technology and domain knowledge—especially in regulated industries like life sciences.<h2>How VMO Supports Life Sciences AI Transformation</h2>At VMO, we help life sciences companies bridge the gap between ambition and execution.Our capabilities include:<ul><li>AI/ML Development: From predictive models to advanced analytics</li><li>Data Engineering &amp; Integration: Connecting siloed systems into unified platforms</li><li>Healthcare &amp; Life Sciences Expertise: Understanding regulatory and domain-specific challenges</li><li>End-to-End Delivery: From strategy to deployment and scaling</li></ul>We work closely with clients to identify the right use cases, build scalable solutions, and ensure compliance at every stage.<h2>Final Thoughts</h2>AI is no longer experimental in life sciences, it’s foundational.The question is no longer&nbsp;if companies should adopt AI, but&nbsp;how fast they can do it effectively.Those who act now will define the future of the industry.

Why Life Sciences Companies Can’t Ignore AI Integration

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Support Function 2026 | Hanoi, Vietnam - Connecting People, Enabling Impact Over the past few days, Hanoi has brought together ALTEN’s global Support Functions,&nbsp;a space where ideas were shared, perspectives aligned, and connections strengthened. As the organizing team, VMO was proud to deliver not just an event, but a platform for real collaboration. From strategic discussions to operational insights, every session reflected a shared goal: driving alignment while embracing diversity across teams. What stood out most was the openness, trust, and strong team spirit built throughout the event,&nbsp; a true reflection of ALTEN’s global strength. A big thank you to all participants and teams involved for making this journey meaningful. We look forward to what we will build together next! #ALTEN #ALTENAsia #VMO #SupportFunction2026 #Leadership #Collaboration #MakeItHappen

Over the past few days, Hanoi has brought together ALTEN’s global Support Functions,  a space where ideas were shared, perspectives aligned, and connections strengthened.

Support Function 2026 - Connecting People, Enabling Impact

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<h2>From Strategy to Daily Operations: How Digital Healthcare Actually Works</h2><h2>The Gap Between Digital Health Strategy and Reality</h2>Every healthcare organization today has a&nbsp;digital strategy. AI roadmaps, data platforms, patient apps, and innovation labs are becoming standard across hospitals and healthcare systems.But strategy alone does not transform healthcare.The real challenge is turning those plans into&nbsp;systems that work inside daily clinical and operational workflows, where doctors, nurses, administrators, and patients interact with technology every day.Digital healthcare only delivers value when it moves beyond strategy and becomes&nbsp;part of everyday healthcare operations.<h2>What “Digital Healthcare Operations” Really Means</h2>Digital healthcare operations sit at the intersection of&nbsp;technology, clinical workflows, and healthcare management.In practice, it means digital systems supporting everyday activities such as:<ul><li>Patient registration and digital intake</li><li>Appointment scheduling and resource allocation</li><li>Clinical decision support during consultations</li><li>Medical imaging analysis</li><li>Insurance verification and claims processing</li><li>Remote monitoring and follow-up care</li></ul>Instead of isolated tools, effective digital healthcare operations rely on&nbsp;connected ecosystems where systems communicate in real time.Typical components include:<ul><li>Clinical systems</li><li>Operational systems</li><li>Data and intelligence layers</li></ul>When these layers integrate properly, digital healthcare moves from&nbsp;strategy slides to operational reality.<h2>The Core Pillars of Digital Healthcare in Daily Operations</h2><h3>1. Workflow Integration</h3>Healthcare environments are&nbsp;workflow-heavy and time-sensitive.Digital tools must integrate directly into clinical workflows rather than add extra steps.Examples include:<ul><li>AI radiology tools embedded directly into PACS systems</li><li>Automated patient intake linked to EHR systems</li><li>Clinical decision support appears during physician consultations</li></ul><h3>2. Interoperability and Data Exchange</h3>Healthcare systems generate massive amounts of data across departments.Without interoperability, these datasets remain fragmented.Effective digital healthcare operations rely on:<ul><li>Standardized healthcare data formats</li><li>API-driven integrations</li><li>Secure data-sharing platforms</li></ul>This allows:<ul><li>Imaging results to be instantly available to specialists</li><li>Patient history accessible across departments</li><li>Insurance systems connected to hospital billing workflows</li></ul><h3>3. AI Embedded into Clinical Decisions</h3>Artificial intelligence becomes truly valuable when it is&nbsp;embedded inside daily workflows, not used as a standalone analytics tool.Examples include:<ul><li>AI models assisting radiologists in detecting anomalies in scans</li><li>Predictive models identifying patients at risk of deterioration</li><li>AI scheduling systems optimizing operating room utilization</li></ul><h3>4. Operational Intelligence for Hospital Management</h3>Hospitals function as complex operational environments with:<ul><li>Staff scheduling</li><li>Bed management</li><li>Equipment allocation</li><li>Emergency response coordination</li></ul>Digital platforms provide&nbsp;real-time operational visibility, enabling healthcare leaders to make faster decisions.Operational intelligence systems can:<ul><li>Forecast patient demand</li><li>Optimize staffing levels</li><li>Reduce waiting times</li><li>Improve resource utilization</li></ul>In this context, digital healthcare is not only about clinical innovation, it is also about&nbsp;running hospitals more efficiently.<h3>5. Patient-Centered Digital Experiences</h3>Modern healthcare systems increasingly extend beyond hospital walls.Digital healthcare operations now include:<ul><li>Telehealth consultations</li><li>Remote patient monitoring</li><li>Digital patient portals</li><li>Mobile health applications</li></ul>These platforms enable:<ul><li>Continuous care outside clinical settings</li><li>Better patient engagement</li><li>Reduced hospital visits for chronic conditions</li></ul>The shift toward patient-centered digital platforms is transforming healthcare from episodic treatment to continuous care management, leading to improved patient engagement and better management of chronic conditions.<h2>The Role of Technology Partners in Healthcare Transformation</h2>Healthcare providers rarely build their entire digital ecosystem internally.Specialized technology partners help healthcare organizations:<ul><li>Develop custom healthcare platforms</li><li>Integrate AI solutions into clinical systems</li><li>Build interoperable data architectures</li><li>Scale digital health solutions securely and efficiently</li></ul>These partnerships allow healthcare organizations to&nbsp;move faster while maintaining compliance and clinical safety.<h2>From Digital Strategy to Operational Impact</h2>Digital healthcare transformation is not achieved through strategy documents alone.It requires turning high-level ambitions into&nbsp;working systems embedded in everyday hospital operations.When done successfully, digital healthcare enables:<ul><li>Faster diagnoses</li><li>More efficient hospital operations</li><li>Improved patient outcomes</li><li>Better collaboration between healthcare providers</li></ul>The future of healthcare will not be defined by isolated digital tools, but by&nbsp;fully connected digital ecosystems supporting every stage of care delivery.Organizations that successfully bridge the gap between strategy and operations will be the ones that truly unlock the potential of digital health.<h2>About ALTEN VMO’s Healthcare Technology Services</h2>Building operational digital healthcare systems requires deep expertise in&nbsp;healthcare workflows, AI technologies, and scalable software engineering.VMO works with healthcare organizations and healthtech companies to:<ul><li>Develop AI-powered healthcare solutions</li><li>Build secure healthcare platforms</li><li>Integrate clinical systems and data infrastructure</li><li>Scale digital health products globally</li></ul>

From Strategy to Daily Operations: How Digital Healthcare Actually Works

From Strategy to Operations: How Digital Healthcare Works

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<ul><li>Collaborate with stakeholders to gather, understand, and document business requirements and objectives in Japanese and translate to English for technical teams.&nbsp;</li><li>Analyze current business processes and identify areas for improvement.&nbsp;</li><li>Develop detailed functional specifications and use cases for technology and process enhancements.</li><li>Conduct gap analysis and feasibility studies for proposed solutions.&nbsp;</li><li>Support project management activities, including planning, timelines, and resource allocation.&nbsp;</li><li>Facilitate workshops, meetings, and presentations to communicate findings and recommendations.&nbsp;</li><li>Set up communication rules and templates for smooth collaboration Solution documentation, powerpoint preparation and regular communication between all parties.</li></ul>

Business Analyst (Japanese & English) - DU35

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<ul><li>As part of the team, you will be enrolled into this most advanced wireless technology development from prototype design to commercial deployment covering every single step in the whole product life cycle chain.&nbsp;</li><li>You as a SW designer will work together with the most talented colleagues from all over the world to finish the feasibility study, systemization, SW design, implementation and integration of our eNodeB product, troubleshooting and resolving critical technical issues and finally getting our products up and running in customer networks.</li></ul>

C++  Developer - DU35

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<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>

Business Analyst - DU35

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<ul style="list-style-type:disc;"><li>Deliver High-Quality Code: Consistently submit well-documented, maintainable, and robust code through Pull Requests (PRs) that include comprehensive tests (unit, integration) and adhere to team coding standards and best practices. Aim for high test coverage (e.g., &gt;80%) on new code.</li><li>Apply Strong Fundamentals: Design and implement features demonstrating strong understanding and application of software architecture principles (e.g., SOLID, clean architecture), efficient database design (schema, indexing, query optimization), and API best practices (RESTful design, security).</li><li>Leverage AI Tools Effectively: Integrate AI coding assistants and tools into your daily workflow to demonstrably improve development speed (e.g., reducing boilerplate, accelerating test writing, assisting debugging) without compromising code quality, maintainability, or introducing unnecessary complexity. Be prepared to share effective prompts and techniques.</li><li>Contribute to System Stability: Keep production rollback rate related to your contributions under 2% by enforcing comprehensive testing, leveraging CI/CD pipelines effectively, and adhering to established DevOps best practices.</li><li>Engage in Collaborative Development: Actively participate in code reviews, providing timely, constructive feedback based on engineering principles and best practices. Respond proactively to feedback on your own PRs, contributing positively to team velocity and knowledge sharing.</li></ul>

Fullstack Developer - DU35

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1.1 Opportunity &amp; Client Engagement<ul><li>Support the sales team in identifying and qualifying new business opportunities.</li><li>Engage with clients to understand their business objectives, challenges, and requirements.</li><li>Participate in defining deal strategy and engagement approaches for different bidding opportunities.</li></ul>1.2 Bid &amp; Proposal Support<ul><li>Lead bidding activities for various RFP/RFI/RFQ opportunities.</li><li>Collaborate with delivery and technical teams to prepare technical proposals, solution documents, estimations, and demos.</li><li>Review and ensure the quality of bid documents and proposal outputs.</li><li>Conduct workshops and presentations about company’s solutions &amp; product demonstration with clients&nbsp;</li><li>Present case study, facilitate discussions between business and technical stakeholders.</li><li>Work with architects and delivery teams to design high-level solution architectures and implementation approaches.</li><li>Define solution scope, assumptions, risks, and dependencies.</li><li>Support effort estimation, cost modeling, and delivery planning.</li><li>Ensure proposed solutions are feasible, scalable, and aligned with delivery capabilities.</li></ul>1.3 Team Development<ul><li>Contribute to the development of the presales team through training, knowledge sharing, and mentoring.</li><li>Support the Presales Manager in team development, culture building, and KPI management.</li></ul>

Presales Team Leader (JP)

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<ul><li>Design, develop, and maintain Java-based backend applications.</li><li>Collaborate with cross-functional teams to define, design, and deliver new features.</li><li>Write clean, efficient, and scalable code using best practices.</li><li>Troubleshoot, debug, and optimize existing applications.</li><li>Stay up to date with platform improvements and leverage the latest technologies to improve service delivery.</li><li>Communicate effectively with internal and external stakeholders during incidents and change management processes. &nbsp;</li></ul>

Java Fullsatck Developer - DU33

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We are seeking an experienced Data Architect with 8+ years of enterprise data engineering and architecture experience to define, design, and govern scalable, secure, and high- performance data platforms across the organization. The role focuses on Azure-based data architecture, database modernization, analytics enablement, and support of AI/ML &nbsp;initiatives at scale. Recruitment Requirements Data Architecture &amp; &nbsp;Strategy - Define and own end-to-end data architecture for OLTP, analytical, and AI/ML workloads. - Establish architecture standards, patterns, and best practices across Group data platforms. - Design scalable, cloud-native architectures supporting current and future auto-scaling needs. - Lead architectural decisions for database modernization and cloud migration initiatives. Platform &amp; Database Architecture - Architect, design, and optimize data platforms across Azure SQL, Azure PostgreSQL, Cosmos DB, and MongoDB. - Define logical and physical data models, schemas, and governance structures. - Guide performance optimization strategies including indexing, partitioning, sharding, and workload isolation. - Ensure high availability, security, disaster recovery, and compliance across environments. Data Engineering &amp; Integration - Architect and govern data pipelines using Azure Data Factory and Databricks. - Support batch and near real-time data processing patterns. - Define orchestration, monitoring, and automation frameworks. - AI / Analytics Enablement

Data Engineer - DU33

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For Fresher:As part of the team, you will be enrolled into this most advanced wireless technology development from prototype design to commercial deployment covering every single step in the whole product life cycle chain. You as a SW designer will work together with the most talented colleagues from all over the world to finish the feasibility study, systemization, SW design, implementation and integration of our eNodeB product, troubleshooting and resolving critical technical issues and finally getting our products up and running in customer networks.Responsibilities<ul><li>Design and prepare test environment, including device under test (DUT) and test instruments</li><li>Design/define test cases to ensure test coverage</li><li>Test case execution, issue TR (trouble report) or CR (change request)</li><li>Trouble-shooting</li><li>Write test report and record</li><li>Version control of SW and HW</li><li>Support to trial and customer site &nbsp;</li></ul>For Experienced:As part of the team, you will be enrolled into this most advanced wireless technology development from prototype design to commercial deployment covering every single step in the whole product life cycle chain. You as a SW designer will work together with the most talented colleagues from all over the world to finish the feasibility study, systemization, SW design, implementation and integration of our eNodeB product, troubleshooting and resolving critical technical issues and finally getting our products up and running in customer networks.Responsibilities<ul><li>Design and prepare test environment, including device under test (DUT) and test instruments</li><li>Design/define test cases to ensure test coverage</li><li>Test case execution, issue TR (trouble report) or CR (change request)</li><li>Trouble-shooting</li><li>Write test report and record</li><li>Version control of SW and HW</li><li>Support to trial and customer site</li></ul>

Baseband (Integration Verification) Tester (All Levels: Fresher to Senior) - Telecom Industry

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For Fresher:As part of the team, you will be enrolled into this most advanced wireless technology development from prototype design to commercial deployment covering every single step in the whole product life cycle chain. You as a SW designer will work together with the most talented colleagues from all over the world to finish the feasibility study, systemization, SW design, implementation and integration of our eNodeB product, troubleshooting and resolving critical technical issues and finally getting our products up and running in customer networks.For Experience:As part of the team, you will be enrolled into this most advanced wireless technology development from prototype design to commercial deployment covering every single step in the whole product life cycle chain. You as a SW designer will work together with the most talented colleagues from all over the world to finish the feasibility study, systemization, SW design, implementation and integration of our eNodeB product, troubleshooting and resolving critical technical issues and finally getting our products up and running in customer networks.

Baseband (L3) Software Design Engineer (All Levels: Fresher to Senior) - Telecom Industry

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For Fresher:&nbsp;As part of the team, you will be enrolled into this most advanced wireless technology development from prototype design to commercial deployment covering every single step in the whole product life cycle chain. You as a SW designer will work together with the most talented colleagues from all over the world to finish the feasibility study, systemization, SW design, implementation and integration of our eNodeB product, troubleshooting and resolving critical technical issues and finally getting our products up and running in customer networks. For Experience:&nbsp;As a Senior 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.

Business Analyst (Japanese & English) - DU35

Business Analyst (Japanese & English) - DU35

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