Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact.

A Day in the Life

We are seeking a Principal AI/Data Science Engineer with strong AI Solutions Engineering and Forward Deployed Engineering capabilities to accelerate the design, validation, productization, deployment, and adoption of AI-enabled healthcare solutions across AI Labs programs and pillars.
This is a hands-on, stakeholder-facing technical leadership role. The individual will work directly with operating units, product teams, domain experts, and engineering organizations to understand ambiguous business and clinical problems, rapidly develop solution approaches, embed with delivery teams, and translate successful prototypes into secure, reliable, and scalable production solutions.
The role combines AI/Data Science solution engineering with full-stack development, frontend and backend engineering, cloud platforms, enterprise integration, data engineering, MLOps, and end-to-end DevSecOps. Working knowledge of Generative AI is preferred and will support emerging requirements involving RAG, AI Agents, copilots, intelligent workflows, and AI-assisted product experiences.
The position will serve as a technical bridge between AI innovation and real-world execution, strengthening delivery velocity, solution adoption, engineering governance, and the sustainable execution capacity of AI Labs at MEIC.

A Day in the Life

As a Principal AI/Data Science Engineer, you will:

•         Engage directly with operating units, product leaders, clinicians, domain experts, and engineering teams to identify high-value problems and convert them into actionable AI solution opportunities.

•         Operate as a Forward Deployed Engineer by embedding with stakeholder and delivery teams, rapidly understanding workflows, data, constraints, and success measures, and driving solutions from discovery through adoption.

•         Lead technical discovery, requirements clarification, feasibility assessments, solution framing, rapid prototyping, demonstrations, and iterative stakeholder validation.

•         Architect and build end-to-end AI-enabled applications spanning user experiences, frontend applications, backend services, APIs, data pipelines, cloud services, model integration, and enterprise systems.

•         Convert proofs of concept and research outputs into secure, maintainable, validated, and production-ready solutions.

•         Collaborate with Data Scientists and AI Engineers to integrate machine learning, computer vision, NLP, analytics, and GenAI capabilities into usable products and workflows.

•         Design reusable solution patterns, reference architectures, accelerators, and platform components that can be scaled across AI Labs programs and pillars.

•         Establish DevSecOps and MLOps practices, including CI/CD, automated testing, infrastructure as code, deployment, monitoring, observability, and release governance.

•         Build stakeholder confidence through clear technical communication, working demonstrations, trade-off discussions, implementation plans, and measurable outcome tracking.

•         Troubleshoot complex issues across application, data, model, cloud, security, and integration layers.

•         Support GenAI use cases including knowledge assistants, RAG-based applications, AI Agents, Agentic AI workflows, copilots, semantic search, and intelligent automation where appropriate.

•         Ensure solutions align with cybersecurity, privacy, responsible AI, quality, regulatory, and enterprise architecture expectations.

•         Mentor engineers, ESP resources, and development partners while raising engineering quality and delivery maturity.

Responsibilities may include the following and other duties may be assigned:

·       AI Solutions Engineering

•         Own the technical solution lifecycle from opportunity discovery and problem definition through architecture, implementation, validation, deployment, adoption, and continuous improvement.

•         Translate business, clinical, product, and operational needs into solution designs, technical requirements, delivery plans, and measurable success criteria.

•         Develop production-oriented AI applications that combine models, data, software, user experience, and enterprise integrations.

•         Create rapid prototypes and demonstrations to validate feasibility, usability, workflow fit, and stakeholder value before scaling.

·       Forward Deployed Engineering

•         Embed with stakeholder teams during critical phases to understand real-world workflows, constraints, data availability, and adoption barriers.

•         Structure undefined problems, identify the shortest path to value, and iterate using direct stakeholder feedback.

•         Configure, integrate, and adapt AI platforms and reusable capabilities to meet program-specific needs without creating unsustainable one-off solutions.

•         Lead technical workshops, solution walkthroughs, demonstrations, pilots, and deployment-readiness discussions.

•         Capture field learnings and convert them into reusable patterns, platform improvements, documentation, and roadmap inputs.

·       Full-Stack, Platform & Integration Engineering

•         Lead the design and development of responsive web applications, workflow tools, dashboards, backend services, APIs, and microservices.

•         Design modular, cloud-native architectures that support extensibility, interoperability, performance, reliability, and reuse.

•         Integrate AI solutions with enterprise applications, data platforms, authentication services, and operational workflows.

•         Apply strong software engineering practices covering design patterns, code quality, testing, version control, documentation, and technical debt management.

·       AI, Data Science, MLOps & GenAI Enablement

•         Operationalize predictive models, computer vision, NLP, analytics, and optimization capabilities.

•         Enable model serving, inference, API integration, experiment tracking, evaluation, monitoring, drift detection, and lifecycle management.

•         Support implementation of RAG, semantic search, vector databases, prompt orchestration, AI Agents, Agentic AI, copilots, and knowledge assistants.

•         Contribute to GenAI evaluation, guardrails, observability, security, responsible AI, human oversight, and governance patterns.

·       DevSecOps & Production Excellence

•         Implement CI/CD pipelines, automated testing, infrastructure as code, containerized deployment, environment management, and release automation.

•         Integrate security scanning, quality controls, traceability, monitoring, logging, telemetry, and observability throughout the lifecycle.

•         Drive operational readiness, incident troubleshooting, performance optimization, root-cause analysis, and continuous improvement.

·       Technical Leadership & Program Enablement

•         Serve as a senior technical anchor for complex AI solution engagements across AI Labs programs and pillars.

•         Lead architecture reviews, technical assessments, risk identification, estimation, dependency management, and release readiness.

•         Communicate options, trade-offs, risks, progress, and outcomes to executives, product leaders, program teams, engineers, and domain stakeholders.

•         Mentor globally distributed teams and strengthen AI Solutions Engineering and Forward Deployed Engineering capabilities.

Required Knowledge and Experience

•         B.E./B.Tech. or Master's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, or a related technical discipline.

•         14 to 18 years of progressive experience in software engineering, AI/Data Science engineering, solution architecture, platform engineering, product engineering, or related technology roles.

•         Proven experience leading end-to-end design and delivery of enterprise-grade applications, platforms, or AI-enabled solutions.

•         Demonstrated ability to work directly with business stakeholders, product teams, customers, or domain experts to solve ambiguous, high-impact problems.

•         Experience rapidly prototyping solutions and progressing successful concepts into production deployments.

•         Strong hands-on experience in full-stack engineering, backend services, cloud technologies, enterprise integration, APIs, and DevSecOps.

•         Experience operationalizing AI, machine learning, Data Science, analytics, computer vision, or NLP capabilities within applications and workflows.

•         Strong technical communication, stakeholder management, problem-solving, and delivery leadership skills.

Required Technical Expertise

•         Solution & Full-Stack Engineering

•         React and/or Angular; JavaScript, TypeScript, HTML5, CSS3, responsive design, and modern UI development.

•         Backend development using one or more of Python, Java, .NET, or Node.js.

•         REST APIs, microservices, distributed systems, application architecture, design patterns, event-driven architectures, and enterprise integration.

•         Rapid prototyping, demonstrations, solution configuration, debugging, testing, and production hardening.

•         Cloud, Platform & DevSecOps

•         Microsoft Azure and/or AWS cloud services and cloud-native application architecture.

•         Docker, Kubernetes, API management, identity and access integration, and infrastructure as code.

•         Azure DevOps and/or GitHub, CI/CD pipelines, automated testing, release automation, and configuration management.

•         Monitoring, logging, telemetry, observability, reliability engineering, cybersecurity integration, and secure software development lifecycle practices.

•         AI, Data Science & MLOps

•         Python and practical familiarity with machine learning, Data Science, analytics, computer vision, NLP, model evaluation, and data pipelines.

•         Integration of AI or machine learning models into applications through APIs, model-serving platforms, or embedded workflows.

•         MLOps, experiment tracking, model versioning, evaluation, deployment, monitoring, and lifecycle considerations.

•         SQL and NoSQL databases, data processing, feature and inference pipelines, and secure data-access patterns.

•         Stakeholder-Facing Technical Delivery

•         Ability to conduct discovery sessions, technical workshops, architecture discussions, demonstrations, and stakeholder reviews.

•         Ability to explain complex technical concepts, alternatives, and trade-offs clearly to technical and non-technical audiences.

•         Ability to operate with incomplete information, prioritize effectively, and drive solutions across organizational boundaries.

Nice to Have

•         Working knowledge of Generative AI, Large Language Models, foundation models, and AI-enabled product development.

•         Exposure to Retrieval-Augmented Generation, GraphRAG, semantic search, vector databases, prompt engineering, AI Agents, Agentic AI, multi-agent systems, and AI copilots.

•         Experience with Azure AI Services, Azure AI Foundry, Azure OpenAI Service, Semantic Kernel, LangChain, LangGraph, LlamaIndex, Hugging Face, Copilot Studio, PyTorch, or TensorFlow.

•         Experience with AI-assisted software engineering and developer-productivity tools.

•         Experience in medical devices, healthcare, digital health, life sciences, or another regulated environment.

•         Knowledge of FDA guidance, ISO 13485, IEC 62304, IEC 82304, SaMD, cybersecurity, privacy, and regulated software development practices.

•         Experience supporting Centers of Excellence, internal consulting teams, platform teams, incubation teams, or forward deployed customer engagements.

Impact of the Role

This position will serve as a critical bridge between AI innovation, stakeholder needs, and enterprise-grade execution. By combining AI Solutions Engineering with Forward Deployed Engineering, the role will accelerate discovery-to-deployment cycles, improve solution adoption, strengthen end-to-end ownership, and convert field learnings into scalable capabilities for AI Labs. The Principal AI/Data Science Engineer will help de-risk strategic objectives and expected outcomes while enabling sustainable, reusable, and high-quality AI delivery across programs and pillars at MEIC.

 

Physical Job Requirements

The above statements are intended to describe the general nature and level of work being performed by employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position. 

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Apply only through official Medtronic channels. All legitimate Medtronic recruiting communications come from approved Medtronic platforms and official @medtronic.com email addresses. 

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Benefits & Compensation

Medtronic offers a competitive Salary and flexible Benefits Package
A commitment to our employees lives at the core of our values. We recognize their contributions. They share in the success they help to create.  We offer a wide range of benefits, resources, and competitive compensation plans designed to support you at every career and life stage.
 

 

 

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This position is eligible for a short-term incentive called the Medtronic Incentive Plan (MIP).