AI Solution Architect

Innovation Engineering (IEN) partners with Business Units and Functions to accelerate innovation and enhance quality, aligning closely with product roadmaps and priorities. We provide a focused portfolio of digital innovation and system platforms that enable our Philips Businesses to scale innovation.

We are seeking an AI Solutions Architect to enable the adoption and acceleration of AI across Innovation Engineering (IEN). This high-impact role is central to our ambition to deeply integrate AI into our engineering workflows across hardware, systems, software development, and platforms.

About the Role

As an AI Solution Architect at Philips, you focus on integrating compliant AI solutions for regulated healthcare and engineering to meet customer needs. Acting as the bridge between AI engineering, software architecture, regulatory requirements, and customer expectations, you guide teams and stakeholders to deliver impactful, value-driven AI capabilities that are effectively adopted by end users. This senior, hands-on role requires both technical depth and strategic vision to drive the successful application and integration of AI across engineering, business, and customer environments.

Key Responsibilities

1. Architecture, Solution Design & Reuse

• Guide engineering teams in using generative AI. Design and integrate full solutions, including LLMs, prompt engineering, agents, and non-LLM apps, from concept to implementation.
• Identify and rank high-value AI use cases, gathering input from domain experts and product managers to align with organizational goals.
• Break down complex AI systems into clear, modular components across data, model, application, and infrastructure layers; set up clear interfaces for independent development.
• Promote reuse of common AI platforms, services, and architectural patterns; create technical blueprints and reusable components for efficiency and consistency.
• Make informed “buy vs. make” choices for AI models and platforms, preferring established industry solutions where suitable.

2. Delivery, Quality & Non-Functional Requirements

• Own and govern architectural standards for safety, performance, scalability, security, privacy, and explainability, and ensure these are included in design, deployment, and operations.
• Guide cross-functional teams through development, testing, and deployment to deliver solutions that are robust, maintainable, and ready for operational use.
• Promote best practices for model evaluation, prompt engineering, and human-in-the-loop feedback; contribute to shared GenAI standards to enhance impact.

3. Healthcare, Compliance & Responsible AI

• Ensure AI solutions comply with all regulatory, security, privacy, and quality standards in safety-critical environments.
• Implement strong AI lifecycle governance, covering data provenance, model validation, monitoring, and thorough documentation.
• Integrate responsible AI principles by design; collaborate with regulatory, quality, privacy, and cybersecurity teams and connect to the RAI team’s established practices.

4. Stakeholder Leadership & Technical Negotiation

• Translate business, clinical, and operational goals into actionable, AI-enabled architectures and MVPs.
• Act as a trusted technical advisor in architecture reviews and decision-making forums.
• Lead technical negotiations and architectural trade-offs among cross-functional stakeholders.
• Influence product roadmaps and investment decisions with evidence-based recommendations and technical expertise.
• Mentor and lead AI, data, and software teams to build organizational capacity across domains.

Education & Experience

• Bachelor’s degree in Information Technology, Computer Science, Engineering, or a related field; a Master’s degree is preferred.
• 10–12+ years of experience in software, systems, or solution architecture roles.
• Over 5 years of hands-on experience deploying AI/ML systems in production environments.
• Proven experience collaborating across various teams, products, or platforms.
• Experience in enterprise-scale and/or regulated environments (e.g., healthcare, medical devices, automotive, aerospace, industrial systems) is highly preferred.
• Hands-on experience in applied Data & AI and software craftsmanship is required.

Technical Expertise

• AI/ML system design, including training pipelines, evaluation, deployment, monitoring, and lifecycle management.
• Deep understanding of generative AI technologies, including LLMs, prompt engineering, retrieval-based generation, and agent patterns.
• Strong hands-on experience with cloud-based AI platforms and modern AI/GenAI toolkits and frameworks.
• Software architecture expertise, including APIs, microservices, and event-driven systems.
• Experience with data platforms and MLOps, such as feature stores, CI/CD for AI, and governance.
• Experience designing cloud, edge, and hybrid architectures (e.g., Kubernetes, Azure/AWS/GCP, embedded Linux).
• Familiarity with AI/ML programming languages and concepts, particularly Python, Rust, or C#, C++, is a plus.
• Strong understanding of security, privacy-by-design, and responsible AI principles.
• Classical computer vision experience (e.g., OpenCV, image preprocessing, object detection) is a strong plus.

Personal Attributes

• Strong systems thinker, able to work across hardware, systems, software, and data domains.
• Communicates complex technical topics clearly to non-technical audiences.
• Organizationally savvy: effective in multi-stakeholder environments, influencing without formal authority.
• Pragmatic and delivery-oriented, focused on real-world impact rather than theory alone.
• Effectively seeks input and shares results both within and outside the team, tailoring communication for audiences ranging from engineers to organizational leadership.
• Collaborative, proactive, motivated to create impact, and energized by enabling others to succeed.

We are looking for a candidate who lives in The Netherlands currently.

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.