Role Summary:
We are looking for a senior technology professional with 15+ years of experience in software engineering, architecture, and enterprise technology, with strong experience across data engineering, machine learning, and AI/Generative AI.
The candidate will be responsible for applying AI to solve meaningful enterprise problems and realize measurable business and engineering value, while architecting and delivering scalable AI-enabled solutions and reusable AI capabilities.
The role requires strong software and data engineering foundations, AI/ML expertise, system architecture, hands-on development, and technical leadership.
Key Responsibilities
- Identify and realize high-value enterprise opportunities through AI, with measurable business and engineering impact.
- Architect and develop AI/ML and Generative AI solutions for enterprise use cases.
- Build reliable data pipelines, data platforms and data processing capabilities that support AI/ML solutions.
- Apply ML techniques to develop, train, evaluate and deploy machine learning solutions.
- Design and develop LLM-based applications, including RAG, agents, tool use, and AI-assisted workflows.
- Integrate AI solutions with existing enterprise platforms, APIs, data sources, and engineering tools.
- Establish practices for AI architecture, evaluation, security, observability, and governance.
- Lead POCs from hypothesis through productionisation, with measurable outcomes.
- Mentor engineers and establish engineering best practices.
Experience & Skills
- 15+ years of software engineering / architecture / technology experience.
- Strong programming skills, particularly Python, and solid software architecture fundamentals.
- Experience designing data pipelines, data processing workflows, data platforms, and data quality practices.
- Practical experience with ML model development lifecycle including data processing, ML model training, evaluation, deployment, and monitoring.
- Strong understanding of AI/ML techniques and modern GenAI approaches, including but not limited to LLMs, prompting, context engineering and inference.
- Practical experience with LLMs, RAG, AI agents, embeddings, and vector databases.
- Experience with modern AI application frameworks and orchestration technologies such as LangChain, LangGraph, Semantic Kernel or equivalent.
- Experience taking AI/ML solutions from PoC to production in enterprise environments.
- Understanding of AI security, evaluation, observability, and governance.
Experience applying AI across the software development lifecycle (SDLC), DevOps and developer productivity is highly desirable.
Skills Required
- 15+ years of software engineering, architecture, or enterprise technology experience
- Strong programming skills, particularly in Python
- Strong software architecture fundamentals
- Experience designing data pipelines, data processing workflows, data platforms, and data quality practices
- Experience with the machine learning model development lifecycle, including data processing, training, evaluation, deployment, and monitoring
- Strong understanding of AI and machine learning techniques and modern generative AI approaches
- Practical experience with LLMs, RAG, AI agents, embeddings, and vector databases
- Experience with AI application frameworks and orchestration technologies such as LangChain, LangGraph, or Semantic Kernel
- Experience taking AI and ML solutions from proof of concept to production in enterprise environments
- Understanding of AI security, evaluation, observability, and governance
- Experience applying AI across the software development lifecycle, DevOps, and developer productivity
Philips Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Philips and has not been reviewed or approved by Philips.
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Retirement Support — Retirement support is positioned as a standout, including a strong 401(k) match (often described at 7%) alongside pensions in some contexts.
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Flexible Benefits — Flexible benefits are emphasized through choice in health insurance options and a broad “Total Rewards” approach that combines compensation, health and wellness, and work-life support.
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Leave & Time Off Breadth — Leave and time off breadth appears strong, with generous paid time off and policies covering parental leave, caregiving responsibilities, volunteering, and family medical leave.
Philips Insights
What We Do
As a leading health technology company, it is our purpose to improve people’s health and well-being through meaningful innovation. Our goal is to improve 2.5 billion lives per year by 2030. We're always looking for ways to make life better – not only for our customers, but also for everyone working here. That's why we're taking steps to create an environment where we all acknowledge and embrace our differences and uniqueness and listen to and value each other's views. When people feel cared for and listened to, they bring their best qualities to work, leading to better collaboration, communication, innovation and success.
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