AI Transformation & Agentic Platforms Senior Leader

Senior Director AI Transformation & Agentic Platforms

Job Description

At Philips we believe that every human matters and that technology can profoundly improve lives when its used with purpose. Our Personal Health business helps people take charge of their health and well-being through intelligent personalized innovations from oral care and grooming to mother & child care and womens health.

Now we are accelerating into a new era: harnessing the power of Artificial Intelligence to transform how we innovate operate and engage consumers while building the foundational AI capabilities that will power the future of Personal Health. Were looking for a visionary and technically grounded AI Transformation Leader to drive this change.

You will own the AI strategy and the AI platform for Personal Health end to end: from executive agenda to reference architecture to what actually ships. This is a player-coach role for a technically credible operator someone who can debate agent-orchestration patterns and evaluation methodology with engineers in the morning and translate them into a business case for the leadership team in the afternoon. You will report into the Personal Health Head of Strategy and Commercial Operational Excellence and you will build a multidisciplinary organization of AI engineers ML engineers data scientists AI architects and platform product managers across Amsterdam and Bengaluru.

Our model strategy is compose not pretrain: we build on frontier models from OpenAI and Anthropic alongside open-weight alternatives consumed through both Microsoft Azure and AWS rather than a single-vendor bet and we differentiate through proprietary data on-device intelligence domain evaluation and consumer trust not by training foundation models from scratch. You will own the multi-model architecture that keeps us portable across suppliers as the frontier moves.

About the Role:

1. The agentic AI platform

Design and stand up the shared AI enablement layer for Personal Health: a model gateway with cost/quality routing across frontier LLMs (OpenAI GPT-class Anthropic Claude) and open-weight models retrieval-augmented generation (RAG) pipelines with vector search over consumer product and scientific knowledge bases and agent orchestration with MCP-style tool integration into our commercial and consumer systems.

Make it safe to ship fast: evaluation harnesses and golden datasets LLM observability and tracing guardrails and human-in-the-loop patterns prompt and model version management and red-teaming as a release gate.

Run the platform like a product: paved-road SDKs and accelerators for product teams token-level cost management (AI FinOps) and adoption metrics that prove reuse beats one-off builds.

2. Edge AI on a global device fleet

Extend our on-device AI lead embedded inference on power- and memory-constrained consumer hardware model compression (quantization pruning distillation) and over-the-air model deployment across a fleet of millions of connected devices.

Champion privacy-preserving learning: on-device processing and federated learning patterns so personalization improves without raw personal data leaving the product.

3. Data foundations

Build the consumer-grade data platform AI needs: lakehouse architecture streaming device telemetry feature stores and consent-aware consumer data products interoperable Philips enterprise data & analytics landscape.

Partner with Enterprise IT Digital & Technology Data & Analytics and R&D so platforms are secure compliant and shared not duplicated.

4. Transformation across the business

Identify and sequence the highest-value AI use cases across product innovation consumer journey costumer engagement and operations and get them up and running.

Lead the Personal Health AI community; raise AI fluency across business and technical teams through capability programs and grow the internal AI talent pipeline through hiring and mentoring.

Define the KPI framework AI maturity platform adoption model performance in production and realized business value and report it to senior leadership with data not anecdotes.

5. Responsible AI by design

Operationalize Philips published AI principles well-being human oversight safety fairness transparency security privacy and sustainability as engineering practice: model documentation bias evaluation incident response and audit-ready traceability.

Own EU AI Act readiness for Personal Health AI use cases (risk classification conformity posture) alongside GDPR and global consumer-privacy obligations and manage the boundary between consumer wellness features and regulated medical functionality.

Your first 12 months:

The shared agentic AI platform is live with the model gateway RAG evaluation and observability layers adopted by at least three product or functional teams.

Acceleration of AI Gameplan with at least 4 new use cases running in production with measured business impact and full Responsible AI documentation.

The two-hub organization is hired and operating with clear platform data and enablement charters.

AI governance is operating at the speed of delivery: use-case triage risk classification and release gates that teams experience as an accelerator not a queue.

Youre the right fit if:

A track record of shipping AI/ML platforms or products at enterprise or consumer scale you have owned systems in production with SLOs incidents and measurable outcomes not only strategy decks.

Deep working fluency in the modern AI stack: LLMs and generative AI agentic frameworks and orchestration RAG and vector search MLOps/LLMOps evaluation and observability and cloud-native architecture we operate across AWS and Microsoft Azure and hands-on experience with services like Amazon Bedrock or Azure AI Foundry is a strong signal.

Experience building and leading multidisciplinary AI organizations (engineers scientists architects platform PMs) including distributed teams across geographies.

Demonstrated ability to run technology-led business transformation in a large matrixed company securing executive sponsorship sequencing a roadmap and landing adoption.

Fluency in responsible AI and data protection in practice: GDPR EU AI Act awareness model governance and privacy-by-design.

Differentiators

Edge/embedded ML experience: on-device inference model compression OTA deployment on consumer hardware.

Federated learning or other privacy-preserving ML in production.

Consumer health HealthTech FMCG or connected-device (IoT) domain experience.

Experience with AI cost engineering at scale token economics model routing for cost/quality GPU capacity strategy.

A public technical footprint: publications patents open-source contributions or conference talks.

Typically this profile comes with 12 years in AI data or platform engineering leadership but we care about what you have shipped and scaled not the exact year count.

How we work together

We believe that we are better together than apart. This role is office-based and this means working in person at least 3 days per week.

About Philips
We are a health technology company. We built our entire company around the belief that every human matters and we wont 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 youre 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.