Data & AI Engineer

Job Title

Data & AI Engineer

Job Description

Data & AI Engineer

In this role, you are part of

MR Service Innovation is building a new Digital & AI team with one mission: keep MRI systems scanning. Every hour an MRI system is down, patients wait. Our team applies AI across the full-service lifecycle — from detecting issues before they cause downtime, to resolving cases remotely, to protecting service margins —as part of our multi-year Autonomous MR journey: a graduated maturity path from Detect to Self-Heal to Predict to Autonomous service.

As a Data & AI Engineer, you will build and productionise the AI solutions behind this journey: predictive models that flag failures before they happen, GenAI assistants that help field and remote service engineers resolve cases faster, and analytics that protect contract revenue and service profitability. You will work with real service data at scale — over 100K historical service and machine-log cases — and see your work deployed to a global installed base of medical imaging systems.

This is a ground-floor opportunity on a newly formed team: high ownership, direct visibility to business leadership, and a global working environment spanning India, the Netherlands, and North America.

Your Role:

• Design, develop, and deploy production-grade Data and AI solutions across the MR service AI portfolio — including predictive failure and remote-resolution models, AI-driven parts recommendation, root-cause analytics for rework reduction, and GenAI applications for contract and entitlement analytics.
• Build and maintain robust data pipelines and data models over service datasets (ITSM cases, remote diagnostics/machine logs, parts and contract data), ensuring reliable, secure, high-quality inputs for AI applications.
• Develop, integrate, and operationalise ML models, LLMs, Retrieval-Augmented Generation (RAG) pipelines, and agentic AI workflows into service tools used by field service engineers and remote service engineers worldwide.
• Take POCs to MVP & productization: harden prototypes (including those built with external AI partners) into validated, monitored, supportable enterprise solutions.
• Implement AI engineering best practices — MLOps/LLMOps, CI/CD, automated testing, monitoring, model versioning, and governance — appropriate to a regulated healthcare environment.
• Develop APIs, microservices, and cloud-native AI services for scalable deployment across enterprise and service platforms.
• Monitor model performance in the field; retrain and continuously optimise for accuracy, latency, cost, and reliability against measurable service outcomes (system uptime, time-to-resolution, first-time-right).
• Embed Responsible AI by design: data privacy, security, explainability, auditability, and compliance requirements consistent with MedTech and Philips AI governance standards.
• Collaborate closely with Data Scientists, System Architects, Service Information Architects, Product Owners, and external delivery partners to translate service business problems into engineered solutions.
• Evaluate emerging AI technologies and frameworks; contribute to code reviews, documentation, knowledge sharing, and the engineering standards of a growing team.

What Makes This Role Different

• Mission with a heartbeat: your models directly reduce MRI downtime — that means faster diagnosis for patients, not just dashboards.
• Full lifecycle exposure: upstream commercial analytics (contracts, entitlements, pricing) and downstream operational AI (prediction, resolution, parts) in one portfolio.
• Real data, real scale: validated service case data across a global installed base — you build on evidence, not toy datasets.
• Builder's seat: a newly carved-out team where engineering standards, tooling choices, and ways of working are still being shaped — by you.

You're the Right Fit If:

Minimum Education

Bachelor's or Master's Degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Software Engineering, Statistics, Mathematics, or a related discipline.

Minimum Experience

• Bachelor's Degree: 3-6 years of experience in Data Engineering, AI Engineering, Machine Learning Engineering, Software Engineering, or related fields.
• OR Vocational/Equivalent Qualification: Minimum 4 years of relevant experience in Data Engineering, AI Engineering, Analytics, Machine Learning, or Software Development.

Preferred Qualifications

• Experience with Azure AI Services, Azure Machine Learning, Microsoft Fabric, or Amazon Bedrock (including model orchestration, agents, and knowledge bases on Bedrock).
• Experience taking Generative AI or agentic AI applications from POC to production in an enterprise setting.
• Exposure to healthcare, MedTech, or other regulated industries; familiarity with medical device service operations, IoT/remote diagnostics data, or field service workflows is a strong plus.
• Knowledge of Responsible AI, AI governance, cybersecurity, and data privacy practices (e.g., GDPR, healthcare data handling).
• Comfort working in a globally distributed, remote-first team across time zones (India, Europe, North America).
• Strong problem-solving skills with excellent collaboration and communication abilities — able to work directly with service domain experts and translate their knowledge into AI solutions.

You will stand out if you also have:

Technical Skills

• Programming: Python (preferred), SQL
• Data Engineering: ETL/ELT, data pipelines, data modelling for structured and unstructured service data (logs, case notes, documents)
• Machine Learning: Scikit-learn, TensorFlow or PyTorch; experience with time-series, classification, and NLP on operational data is a plus
• Generative AI: LLMs, prompt engineering, RAG, vector databases, agentic AI frameworks; experience with managed GenAI platforms such as Azure OpenAI Service or Amazon Bedrock
• Cloud Platforms: Azure (preferred), AWS or GCP
• MLOps/LLMOps: MLflow, Docker, Kubernetes, GitHub Actions/Azure DevOps, CI/CD
• Databases: SQL, NoSQL, vector databases
• API Development: REST APIs, FastAPI or similar frameworks
• Version Control: Git

How we work together:

We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.

• Onsite roles require full-time presence in the company’s facilities.
• Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
• This role is an office-based role.

About Philips: We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.

• Learn more about our business.
• Discover our rich and exciting history.
• Learn more about our purpose.

If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.

#LI-PHILIN