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?

hype-or-the-next-evolution-of-medical-training

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

ai-benchtalks-04-ai-for-advanced-metal-3d-printing

<h2>Will Your Next Doctor Be an Avatar? Exploring the Future of AI-Powered Patient Engagement</h2>Imagine opening your healthcare app late at night with a question about your medication. Instead of waiting for office hours, a lifelike virtual healthcare assistant appears, listens to your concern, and responds in natural conversation—while understanding your medical history and knowing when to escalate you to a clinician.This future is closer than many people think.Advances in generative AI, natural language processing (NLP), and digital human technology are making AI avatars a valuable extension of modern healthcare. While they won't replace doctors, they are changing how providers connect with patients, making healthcare more accessible, responsive, and personalized.<h2>Why Patient Engagement Is Evolving</h2>Patients increasingly expect the same convenience from healthcare that they experience in banking, retail, and other digital services. At the same time, providers face growing challenges, including clinician shortages, rising patient volumes, and increasing administrative workloads.AI-powered avatars help bridge this gap by providing instant support, reducing routine administrative tasks, and keeping patients engaged throughout their care journey.<h2>How AI Avatars Are Being Used Today</h2>Healthcare organizations are already exploring AI avatars across multiple touchpoints.Virtual assistants can answer common questions about medications, appointments, hospital services, and post-discharge care, giving patients immediate guidance while reducing pressure on support teams.Before appointments, avatars can collect symptoms and medical history updates, allowing clinicians to focus more on diagnosis and treatment rather than paperwork. After treatment, they can check in with patients, remind them to take medication, monitor recovery, and flag concerns that require medical attention.For patients living with chronic conditions such as diabetes or hypertension, AI avatars also provide ongoing coaching, health education, and personalized reminders that encourage better long-term adherence.<h2>Why Digital Humans Feel Different from Chatbots</h2>Unlike traditional chatbots that rely on text, AI avatars combine conversational AI with realistic voice and facial expressions to create a more natural interaction.Patients can simply speak instead of type, making healthcare more accessible for older adults and those with limited digital literacy. This human-like experience also helps build trust and encourages patients to ask questions they might otherwise hesitate to raise.Behind these interactions are technologies such as Large Language Models (LLMs), speech AI, computer vision, and secure integration with Electronic Health Records (EHRs), enabling conversations that are both intelligent and context-aware.<h2>Challenges That Can't Be Ignored</h2>As promising as AI avatars are, they must be implemented responsibly. Healthcare organizations need to ensure patient data remains secure, comply with regulations such as HIPAA and GDPR, and make it clear when patients are interacting with AI rather than a human clinician.Equally important, AI should support not replace medical professionals. The best solutions are designed to handle routine interactions while seamlessly escalating complex or urgent cases to healthcare providers.<h2>The Future Is Collaborative</h2>The next generation of healthcare won't replace doctors with avatars. Instead, AI will become the digital front door to care, helping patients schedule appointments, answer routine questions, complete pre-visit assessments, and receive personalized follow-up support.By automating repetitive interactions, clinicians can spend more time on what matters most: delivering expert, compassionate care.At&nbsp;VMO, we help healthcare organizations build secure, AI-powered digital health solutions from intelligent virtual assistants and patient engagement platforms to healthcare data engineering and system integration. By combining AI expertise with healthcare domain knowledge, we enable providers to create smarter, more connected patient experiences while keeping clinicians at the heart of care.As AI continues to evolve, the question may no longer be&nbsp;whether you'll interact with a healthcare avatar but how it can make your healthcare journey faster, easier, and more personalized.

Exploring the Future of AI-Powered Patient Engagement

exploring-the-future-of-ai-powered-patient-engagement

<h2>Model Context Protocol (MCP): The Foundation Powering the Next Generation of AI Agents</h2><h2>1. Why is everyone talking about MCP?</h2><ul><li>AI has evolved from chatbots to AI Agents capable of reasoning, planning, and executing complex tasks.</li><li>As organizations adopt AI Agents in real-world environments,&nbsp;connectivity has become a major challenge.</li><li>AI Agents need secure access to enterprise data, business applications, and external tools, but custom integrations are difficult to scale.</li><li>Introduced by Anthropic in 2024 and now governed by the Linux Foundation,&nbsp;Model Context Protocol (MCP) provides a common protocol for communication between AI models and external tools, applications, and data sources.</li><li>Rather than being another AI model, MCP serves as the infrastructure that enables AI to operate effectively in real-world environments.</li></ul><h2>2. Why does MCP matter?</h2>MCP is often described as&nbsp;"USB-C for AI" because it standardizes how AI systems connect to external resources.This does&nbsp;not mean direct connections to databases, APIs, or other tools are no longer possible—they remain a valid approach and continue to work well in many scenarios.What MCP addresses is the duplication of integration effort. Without a shared protocol, development teams often rebuild similar integration logic for every AI platform or tool that needs access to the same enterprise systems. As the number of AI platforms grows, these integrations become increasingly difficult to maintain.With MCP, developers can build a connector once and reuse it across multiple AI platforms that support the protocol instead of reimplementing the same integration repeatedly.More importantly, MCP expands what AI can actually do in enterprise environments.Instead of relying solely on knowledge learned during training, AI can:<ul><li>Access and format real-time enterprise data for LLMs.</li><li>Connect securely to company resources with governed access and permissions.</li><li>Take actions across multiple systems through standardized tool integrations.</li><li>Automate end-to-end business workflows rather than completing isolated tasks.</li></ul>In other words, MCP enables the transition from conversational AI to action-oriented AI Agents.<h2>3. Why do enterprises choose MCP over direct integrations?</h2>If direct integrations already work, why do many organizations introduce an intermediate layer such as MCP?Several architectural considerations make this approach attractive for enterprise environments:<ul><li>Security &amp; access control – Rather than exposing database credentials directly to AI models, MCP enables organizations to limit permissions, control access, and keep credentials hidden.</li><li>Abstraction &amp; maintainability – Database schemas and APIs evolve over time. MCP abstracts implementation details so backend changes only require updating the connector rather than every AI application.</li><li>Data safety &amp; correctness – Instead of allowing AI to generate arbitrary queries, MCP exposes predefined tools and validates operations, reducing the risk of invalid queries, unintended data modification, or security vulnerabilities.</li><li>Observability &amp; compliance – A standardized protocol makes it easier to log, monitor, and audit how AI Agents access enterprise resources, supporting governance and regulatory requirements.</li><li>Reusability – Connectors built with MCP can be reused across multiple AI models and applications instead of being tightly coupled to a single implementation.</li></ul>Ultimately, MCP does not replace direct integrations. It provides a standardized, secure, and reusable way for AI systems to interact with enterprise resources at scale.<h2>💬 Expert Insight</h2>"With the rise of AI coding agents as the first wave of AI-accelerated programming, the next wave will accelerate the entire software development lifecycle—from writing documentation to testing and deployment. MCP removes much of the integration friction that has traditionally limited wider enterprise adoption."— Tech Lead AI, VMO<h2>4. How is the MCP ecosystem evolving?</h2>Since its introduction, MCP has gained rapid adoption across the AI ecosystem.Leading AI assistants—including Claude, ChatGPT, and Gemini—are building support for MCP alongside developer tools such as Claude Code, Cursor, VS Code, Windsurf, and Codex CLI.Enterprise platforms including IBM BeeAI, Microsoft Copilot Studio, and Postman are also integrating MCP into their AI offerings.The protocol itself continues to evolve in three key directions:<ul><li>Scalability: Supporting stateless architectures and distributed deployments.</li><li>Agent-to-Agent Collaboration: Enabling AI Agents to delegate tasks, retrieve results asynchronously, and recover from failures.</li><li>Enterprise Readiness: Strengthening security, observability, authentication (SSO), and configuration management for production environments.</li></ul>Together, these developments are positioning MCP as a foundational layer for enterprise AI infrastructure.<h2>5. What does MCP enable in practice?</h2>The value of MCP becomes clear through real-world applications.<ul><li>Scientific Research: PapersFlow enables AI to search hundreds of millions of research papers while verifying citations directly from developer workflows.</li><li>Software Engineering: Claude Code can generate applications from Figma designs by interacting with external development tools through MCP.</li><li>Enterprise Productivity: AI Agents can connect with Google Calendar, Slack, GitHub, and Notion to automate scheduling, summarize project updates, and execute business workflows.</li><li>Enterprise Transformation: Riyadh Air has partnered with IBM to develop AI-native operational capabilities powered by autonomous AI Agents.</li></ul>These examples illustrate a broader trend: AI is moving beyond conversation toward execution.6. What opportunities does MCP create for Vietnamese IT companies?For technology companies, MCP represents more than a new protocol—it represents a new layer of the AI technology stack.Because MCP is an open standard, Vietnamese software companies can develop MCP Servers, AI tools, and enterprise integrations that work across multiple AI platforms instead of being tied to a single ecosystem.Beyond software outsourcing, this creates opportunities to deliver AI Agent solutions, enterprise AI integration services, and production-ready AI infrastructure.Internally, engineering teams can also improve development productivity by leveraging MCP-enabled tools such as Claude Code and Cursor, reducing integration effort and focusing on core business logic.Finally, as MCP continues to evolve under the Linux Foundation, developers also have the opportunity to contribute directly to an emerging global open standard.<h2>Conclusion</h2>Model Context Protocol is not simply another AI technology.It is an open standard that enables AI systems to securely interact with external tools, enterprise applications, and real-world data through a common interface.As AI adoption shifts from conversational assistants toward autonomous AI Agents, interoperability is becoming just as important as model intelligence.In that context, MCP is emerging as one of the foundational technologies enabling the next generation of enterprise AI

Model Context Protocol (MCP): The Foundation Powering the Next Generation of AI Agents

The Foundation Powering the Next Generation of AI Agents

the-foundation-powering-the-next-generation-of-ai-agents

<h2>What if healthcare could prevent a heart attack before the first symptom ever appears?</h2>For decades, cardiovascular care has largely been reactive. Patients visit a doctor after experiencing chest pain or shortness of breath, or when routine screenings reveal elevated risk factors. While preventive medicine has made significant progress, predicting who will experience a heart attack years in advance has remained an enormous challenge.By analyzing millions of health records, medical images, genetic markers, wearable device data, and lifestyle patterns, AI is helping healthcare providers identify subtle warning signs that humans simply cannot detect. Rather than waiting for disease to manifest, healthcare is moving toward a future where risks are anticipated, and interventions happen years earlier.This shift represents more than technological advancement. It marks the emergence of&nbsp;Preventive Intelligence: using AI to continuously assess health risks, personalize prevention strategies, and improve patient outcomes before critical events occur.<h2>Why Traditional Risk Assessment Isn't Enough</h2>Most cardiovascular risk assessments rely on established clinical indicators, including:<ul><li>Blood pressure</li><li>Cholesterol levels</li><li>Smoking history</li><li>Diabetes</li><li>Age and family history</li><li>Body Mass Index (BMI)</li></ul>While these factors remain valuable, they provide only part of the picture.Two individuals with nearly identical clinical profiles may experience vastly different outcomes. One may remain healthy for decades, while another suffers a cardiac event within a few years.The missing insight often lies within highly complex relationships across thousands of variables, relationships that AI models are particularly effective at identifying.<h2>How AI Predicts Cardiovascular Risk Earlier</h2>Modern AI systems combine multiple sources of health information to build far more comprehensive risk profiles than traditional scoring models.<h3>Medical Imaging</h3>AI can detect microscopic changes in CT scans, retinal images, echocardiograms, and cardiac MRI scans that may indicate early vascular disease long before symptoms develop.Researchers have demonstrated that AI algorithms can uncover hidden biomarkers that are often invisible during routine clinical interpretation.<h3>Electronic Health Records</h3>Years of clinical history provide valuable predictive signals.AI analyzes:<ul><li>Medication history</li><li>Previous diagnoses</li><li>Laboratory trends</li><li>Hospital admissions</li><li>Physician notes</li><li>Family history</li></ul><h3>Wearable Devices</h3>Smartwatches and fitness trackers continuously monitor:<ul><li>Heart rate variability</li><li>Resting heart rate</li><li>Physical activity</li><li>Sleep quality</li><li>Blood oxygen levels</li><li>Cardiac rhythm abnormalities</li></ul>Continuous monitoring allows AI to identify gradual physiological changes that may otherwise go unnoticed.<h3>Genetics and Biomarkers</h3>When combined with genomic information and advanced laboratory biomarkers, AI can estimate inherited cardiovascular risk with increasing accuracy.Rather than relying on a single genetic mutation, machine learning evaluates thousands of genetic variants alongside environmental and behavioral factors.<h2>From Reactive Care to Preventive Intelligence</h2>The real value of AI lies not in predicting disease but in enabling earlier intervention.Imagine a patient whose AI-generated risk profile indicates a high likelihood of a cardiac event within the next decade.Instead of waiting until symptoms appear, clinicians could recommend:<ul><li>Lifestyle coaching</li><li>Nutrition programs</li><li>Exercise interventions</li><li>Earlier cholesterol management</li><li>Blood pressure optimization</li><li>More frequent monitoring</li><li>Personalized follow-up schedules</li></ul><h2>Digital Twins: Simulating Future Health</h2>One of the most exciting developments is the rise of&nbsp;digital twins in healthcare.A digital twin is a virtual representation of a patient's physiological condition that evolves using real-world health data.By combining:<ul><li>Medical history</li><li>Imaging</li><li>Wearable data</li><li>Laboratory results</li><li>Lifestyle information</li></ul>AI can simulate how cardiovascular health may change under different scenarios.Clinicians may eventually compare the impact of medication, dietary changes, exercise, or smoking cessation before treatment even begins, allowing for more personalized preventive strategies.<h2>The Challenges That Must Be Solved</h2>Despite impressive progress, AI-powered prevention still faces important hurdles.<h3>Data Quality</h3>Predictive models are only as reliable as the data they learn from. Missing, inconsistent, or biased datasets can reduce accuracy and fairness across different populations.<h3>Explainability</h3>Healthcare professionals need to understand&nbsp;why an AI system identifies a patient as high risk. Transparent, explainable AI is essential for building clinical trust and supporting informed decision-making.<h3>Privacy and Security</h3>Predictive healthcare depends on highly sensitive patient information. Strong governance, encryption, regulatory compliance, and responsible data stewardship remain critical to protecting patient privacy.<h3>Clinical Validation</h3>AI should enhance, not replace clinical expertise. Predictive models must undergo rigorous validation across diverse populations before becoming part of routine care.<h2>What This Means for Healthcare Organizations</h2>As preventive intelligence matures, healthcare providers and life sciences organizations have an opportunity to redesign care around proactive intervention rather than reactive treatment.Key areas of investment include:<ul><li>AI-powered clinical decision support</li><li>Secure healthcare data platforms</li><li>Interoperable electronic health records</li><li>Remote patient monitoring solutions</li><li>Predictive analytics infrastructure</li><li>Explainable AI governance frameworks</li></ul>Organizations that establish these capabilities today will be better positioned to deliver more personalized, efficient, and value-based care in the years ahead.<h2>Looking Ahead</h2>AI is unlikely to predict every heart attack with perfect accuracy—and it shouldn't be viewed as a replacement for clinical judgment. However, its ability to uncover hidden patterns across vast amounts of health data is transforming how cardiovascular risk is understood and managed.The future of healthcare will be defined not only by better treatments, but by the ability to identify risk earlier, intervene sooner, and support healthier lives before disease progresses.The question is no longer whether AI will play a role in preventive medicine.It's how quickly healthcare organizations can integrate predictive intelligence into everyday clinical practice.As AI, wearable technology, and precision medicine continue to converge, prevention may become one of healthcare's most powerful innovations—helping shift the focus from treating illness to preserving long-term health.

Can AI Predict a Heart Attack 10 Years Before It Happens? The Rise of Preventive Intelligence

Can AI Predict a Heart Attack 10 Years Before It Happens?

can-ai-predict-a-heart-attack-10-years-before-it-happens

On July 8, 2026, VMO joined its strategic partner, Yokogawa Electric Corporation, in the Scholarship Award Ceremony held at the Posts and Telecommunications Institute of Technology (PTIT), honoring outstanding students from the Applied Information Technology Program.The ceremony recognized students who have demonstrated exceptional academic performance, technical capabilities, and remarkable achievements in technology competitions. More than a celebration of excellence, the event reaffirmed a shared commitment to empowering Vietnam's next generation of technology professionals and encouraging them to pursue innovation with a global mindset.Representatives from Yokogawa, VMO, and PTIT gathered to congratulate the scholarship recipients and celebrate their accomplishments. The event also provided an opportunity for students to connect with industry leaders and gain insights into the skills, mindset, and opportunities needed to thrive in the rapidly evolving global technology landscape.Strengthening the Connection Between Industry and EducationThe scholarship program reflects the shared vision of Yokogawa, VMO, and PTIT in fostering closer collaboration between academia and industry. By recognizing talented students and supporting their professional growth, the initiative contributes to developing a highly skilled workforce ready to meet the demands of the digital era.At VMO, we believe that nurturing young talent is essential to building a sustainable technology ecosystem. Collaborating with leading global partners and educational institutions enables us to create meaningful opportunities for students while helping bridge the gap between academic learning and real-world industry experience.A Partnership Built on a Shared VisionThe Scholarship Award Ceremony reflects the continued commitment of VMO and Yokogawa Electric Corporation to fostering innovation and developing future technology talent. As strategic partners, both organizations remain dedicated to expanding collaboration across education, talent development, and technology initiatives, creating lasting value for students, the industry, and the broader Vietnam-Japan innovation ecosystem.

VMO and Yokogawa Electric Corporation Celebrate Outstanding PTIT Students at Scholarship Award Ceremony

VMO & Yokogawa Electric Corporation Award PTIT Scholarships

vmo-yokogawa-electric-corporation-award-ptit-scholarships

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

java-developer-english-good-du33

<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

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<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>

Data Engineer - DU33 - ENG GOOD

data-engineer-du33-eng-good

About the RoleThis is more than just a data labeling position. In addition to producing high-quality annotations, you will contribute to building annotation workflows, quality standards, and project guidelines that directly impact the performance of AI models in the IoT and smart device domain.This role offers a clear career path toward Quality Control (QC) Lead and Data Operations positions.Key Responsibilities<ul><li>Accurately annotate various types of data, including images, videos, sequential data, and point cloud data, according to project requirements.</li><li>Ensure annotated datasets meet high standards of accuracy, consistency, and completeness.</li><li>Continuously improve existing annotation workflows to enhance efficiency and productivity.</li><li>Contribute to developing annotation guidelines and quality control (QC) standards.</li><li>Collaborate closely with team members to deliver projects on schedule.</li><li>Train and onboard new team members.</li></ul>

Data Annotation Specialist

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Position Overview&nbsp; &nbsp; We are looking for experienced Senior Software Testers to join cross-functional engineering teams working on advanced 5G/LTE Radio Access Network software products.&nbsp; &nbsp; The role focuses on software integration, verification, test automation, system analysis, and quality assurance activities for Radio Unit Application Software (ASW) and Baseband Software (BBSW) platforms.&nbsp; &nbsp; You will work closely with developers, system experts, technical leads, and project managers in an Agile R&amp;D environment to ensure high-quality telecom-grade software delivery.&nbsp; Key Responsibilities&nbsp;<ul><li>Design, execute, and maintain software verification and integration test activities.</li><li>Analyze system requirements and define comprehensive test scenarios and test strategies.&nbsp;</li><li>Develop and maintain automated test frameworks, scripts, and verification tools.&nbsp;</li><li>Perform feature verification, integration testing, regression testing, and system validation.&nbsp;</li><li>Work closely with development teams to identify, analyze, and troubleshoot software issues.</li><li>Support continuous integration and automated test execution environments.&nbsp;</li><li>Analyze system behaviors, logs, and performance results to identify defects and improvements.&nbsp;</li><li>Participate in root cause analysis and Trouble Report (TR) resolution activities.&nbsp;</li><li>Ensure testing coverage and quality standards across software releases.&nbsp;</li><li>Contribute to process improvements, automation efficiency, and testing best practices.&nbsp;</li><li>Collaborate effectively with distributed global engineering team.&nbsp;</li></ul>

Integration and Verification testing

integration-and-verification-testing

Our business environment relies heavily on software for many functions - from intelligent monitoring systems to automation processes, and cloud computing continues to allow us to modernize, consolidate IT infrastructure, automate workloads and pursue next-generation innovation. To continue this transformation, we’re seeking engineer with expertise in software technology, firm grasp of emerging technologies, platform, applications, and ability to customize to help our business become more secure and efficient. You’ll participate in all aspects of the desktop/web development lifecycle for solution including designing, implementing, building, deploying, testing, and troubleshooting market issues to ensure high standard of application performance, uptime, scale, code quality and thoughtful design.Responsibility<ul><li>Software Development: Designing, developing, testing, deploying and maintaining modular- based systems in accordance with best practices.</li><li>Problem Solving: Troubleshoot incidents, identify root cause, fix and document problems, and implement preventive measures.</li><li>Agile Scrum Practices: Participate actively in Agile Scrum processes, including sprint planning, daily stand-ups, retrospectives, and demos.</li><li>Collaboration: Work closely with cross-functional teams, including product management, design, and QA to ensure seamless integration and delivery.</li><li>&nbsp;Documentation: Maintain accurate and up-to-date technical documentation of software components and systems.</li></ul>

Fullstack .Net WPF

fullstack-net-wpf

<ul><li>Break down main requirements together with the teams and ensure clear scope alignment.&nbsp;</li><li>Work closely with surrounding organizations and coordinate teams across geographical locations.&nbsp;</li><li>Guide teams in implementing architecture, functionality, and features.&nbsp;</li><li>Ensure the backlog is visible, transparent, prioritized, and well understood.&nbsp;</li><li>Own end-to-end delivery responsibility from design to integration and verification.&nbsp;</li><li>Plan milestones, manage dependencies, and track progress together with the teams.&nbsp;</li><li>Proactively manage risks, issues, and escalations.&nbsp;</li><li>Secure required resources (people and equipment) with support from line and program management.&nbsp;</li><li>Ensure quality, timeline adherence, and predictable delivery.&nbsp;</li><li>Drive continuous improvement in products, processes, and ways of working.&nbsp;</li></ul>

OPC Executive

OPC Executive

INFORMATION