AI Solution Architect - Generative & Agentic AI

Join Philips Innovation Engineering (IEN) and help teams redesign their workflows using Generative and Agentic AI. In this role, you'll work directly with engineers to identify where AI creates value and enable them to adopt it in their daily work, moving from idea to real outcomes. You won't build solutions in isolation; you'll coach, architect, and enable teams to scale AI adoption across IEN Your role Join Philips Innovation Engineering as an AI Solution Architect, enabling engineering teams to redesign their workflows using Generative and Agentic AI. In this role, you will work hands-on with engineering teams across IEN, helping them identify where AI can create value and supporting them in redesigning their workflows to fully benefit from AI. Rather than building solutions in isolation, you act as a coach, architect, and enabler, ensuring teams can successfully adopt and scale AI in their daily work. You will leverage various tools depending on the needs of each use case including low-code tools such as Microsoft Copilot Studio, ChatGPT Codex, and Anthropic Cowork, as well as pro-code AI platforms such AWS Bedrock and Azure AI Foundry to help teams move from idea to practical application-ranging from engineering productivity improvements to AI-powered workflows and agents. The role operates in a federated model, working closely with AI Champions embedded across departments. Key Responsibilities

1. Workflow Redesign & AI Opportunity Identification

• Partner with engineering teams to analyze and redesign workflows using Generative and Agentic AI
• Identify where AI can drive improvements in productivity, quality, and engineering outcomes
• Facilitate discovery workshops to translate challenges into practical AI-enabled workflow changes

2. Enablement & Hands-on Support

• Work side-by-side with teams to apply AI in their day-to-day engineering activities
• Support teams in building and adopting solutions using low-code and pro-code tools
• Guide teams from discovery * PoC * MVP * early production

3. Solution Design & Structuring

• Help teams define simple, scalable solution approaches for AI-enabled workflows
• Translate use cases into clear implementation approaches and MVP plans
• Support buy-vs-build decisions for platforms, tools, and models
• Ensure solutions are reusable and aligned with broader IEN practices

4. Scaling & Reuse Across IEN

• Capture and share reusable patterns, workflows, and lessons learned
• Support scaling of successful approaches across departments
• Contribute to Accelerator playbooks, demos, and cross-learning

5. Responsible AI & Governance

• Ensure teams apply privacy, security, and responsible AI principles
• Align solutions with enterprise guidelines and engineering standards
• Help teams make informed decisions on safe and compliant AI usage

6. Collaboration in a Federated Model

• Work closely with AI Champions within each department
• Collaborate with Data & AI Engineering and Group IT as needed
• Act as a bridge between central expertise and local team adoption, * Engineering teams successfully redesign their workflows to leverage AI
• AI is adopted in daily work, not just in isolated pilots
• Teams feel confident and enabled to use Generative and Agentic AI themselves
• Successful approaches are reused and scaled across IEN
• AI adoption leads to measurable improvements in productivity and engineering outcomes

• 8-10+ years of experience in engineering, software, systems, or digital transformation roles

• Proven experience applying Generative AI, Agentic AI, or automation in real-world environments

• Strong ability to:

• Analyze and redesign workflows

• Translate abstract opportunities into practical solutions

• Hands-on experience with modern AI tools, preferably including:

• Low-code: Copilot Studio, ChatGPT Codex, Anthropic Cowork tools

• Pro-code: AWS Bedrock, Azure AI Foundry or similar platforms

• Strong stakeholder and facilitation skills, with experience working across diverse teams

• Ability to guide teams through change and adoption, not just technology delivery Preferred Experience

• Experience in engineering-heavy environments (software, systems, manufacturing, etc.)

• Experience working in federated organizations or transformation programs

• Exposure to enterprise AI platforms and governance frameworks

• Experience coaching teams or enabling capability building