Agentic AI Engineer – Generative & Agentic AI

De positie

Join Philips Innovation Engineering as an Agentic AI Engineer and help engineering teams operationalize Generative AI and Agentic AI in real engineering workflows. In this role, you will work hands-on to make data AI-ready, integrate enterprise systems, and transform AI prototypes into robust, scalable solutions embedded into daily engineering practices.

Working closely with AI Solution Architects, AI Champions, and engineering teams, you will ensure AI solutions are not only built, but successfully integrated, adopted, and scaled across the organization.

Your Role

As an Agentic AI Engineer, you will help engineering teams build, scale, and operationalize AI solutions based on Generative AI and Agentic AI.

You will:

• Work closely with engineering teams to make their data AI-ready.
• Enable integration across enterprise systems and engineering workflows.
• Productize AI solutions developed together with the AI Solution Architect.
• Act as a hands-on technical partner, helping teams move from prototype to robust, production-ready solutions.

You will work across both low-code and pro-code environments, leveraging technologies such as:

• Low-code AI
• Microsoft Copilot Studio
• ChatGPT Codex
• Anthropic Cowork
• Pro-code AI Platforms
• Azure AI Foundry
• AWS Bedrock

The role operates within a federated AI model, collaborating closely with AI Champions while partnering with the AI Solution Architect.

What Success Looks Like

Success in this role means:

• Engineering teams have AI-ready data foundations that enable reliable AI use cases.
• AI solutions are embedded into enterprise systems and engineering workflows rather than existing as isolated tools.
• MVPs are successfully transformed into scalable, reusable production solutions.
• APIs and MCPs enable seamless integration across workflows.
• AI solutions demonstrate strong performance, reliability, observability, and cost efficiency.
• Reusable components and implementation patterns accelerate AI adoption across Innovation Engineering (IEN).

Over het bedrijf

With a growing presence in cardiology, oncology, and women's health, Philips operates in the areas of Imaging Systems, Patient Care & Clinical Informatics, Home Healthcare and Customer Services. Philips combines its clinical expertise and human insights to create innovative solutions across the continuum of care, in partnership with clinicians and our customers, to provide better value and expand access to care for millions. Our teams are working hard every day to improve patient outcomes all the way from disease prevention and screening to diagnosis, treatment, therapy monitoring, and disease management. Irrespective of whether the care cycle takes the patient from doctor's office to hospital or hospital to home, or simply from one medical department to another, Philips Healthcare's unique medical solutions are designed to optimize the quality and flow of patient information and clinical decision making.

Wat breng jij

Key Responsibilities

1. Data Readiness & AI Foundations

• Support engineering teams in preparing high-quality data for AI applications by ensuring data is:
• Well-curated and structured
• Available in the correct format
• Stored appropriately (databases, knowledge bases, etc.)
• Complete, consistent, and high quality
• Additionally, you will:
• Identify and resolve data quality issues and gaps.
• Enable data to be effectively used for Generative and Agentic AI use cases.

2. Data Integration & System Enablement

• Design and implement integrations that connect AI solutions with enterprise systems by:
• Developing integrations using APIs and MCPs (Model Context Protocols).
• Connecting AI solutions with engineering tools and enterprise knowledge sources.
• Ensuring reliable, scalable, and secure access to data across workflows.

3. Co-Creation & Enablement with Engineering Teams

• Partner directly with engineering teams to successfully implement AI solutions by:
• Working side-by-side with teams during implementation.
• Supporting both low-code and pro-code AI development approaches.
• Guiding teams from:
• Prototype
• Working solution
• Daily operational usage

4. Productization & Scaling

• Transform MVPs and prototypes into reusable enterprise solutions by ensuring they are:
• Production-ready
• Reliable
• Maintainable
• Scalable across teams and use cases
• Fully integrated into engineering workflows
• You will also package solutions for reuse across Innovation Engineering (IEN).

5. Implementation Alignment & Technical Choices

• Work closely with the AI Solution Architect to:
• Align on technology stacks.
• Define deployment models.
• Select appropriate LLMs.
• Maintain consistency and reusability across AI solutions.
• Contribute to engineering standards and implementation best practices.

6. Operational Excellence (AgentOps)

• Apply AgentOps practices to continuously improve AI solutions by focusing on:
• Monitoring
• Performance
• Robustness
• Cost optimization
• Evaluation frameworks
• Feedback loops
• Observability

7. Responsible AI & Engineering Standards

• Ensure AI implementations comply with enterprise standards by:
• Following security and privacy requirements.
• Applying Responsible AI principles.
• Aligning with governance frameworks.
• Contributing reusable components, integration patterns, and engineering playbooks.

Required Qualifications

• Strong experience building AI-enabled applications, integrations, or data-driven systems.
• Solid software development experience, including Python.

Experience developing:

• Integrations
• Services
• Automation solutions

Proven ability to:

• Structure and prepare data for AI use cases.
• Integrate systems using APIs.
• Take AI solutions from prototype to production.
• Strong collaboration skills and experience working directly with engineering teams.