Help Shape the Future of Healthcare Data at Philips:

At Philips, we believe every human matters. Our mission is to ensure access to quality healthcare for everyone, everywhere. As a Data Engineer, you’ll play a key role in designing and maintaining the data pipelines and infrastructure that power innovative healthcare technologies.

This is an opportunity to work with cutting-edge AI, data science, and cloud technologies, collaborating with talented engineers, data scientists, and product teams to deliver impactful solutions that improve lives.

What You’ll Be Doing:

• Design, build, test, and maintain scalable data pipeline architectures in collaboration with Data Architects.
• Create infrastructure for extracting, transforming, and loading (ETL) data from diverse sources using SQL and AWS technologies.
• Work closely with product teams to translate data needs into technical system requirements.
• Build complex queries and data pipelines that clean, transform, and aggregate information from multiple sources.
• Optimize ETLs for accuracy, stability, availability, and performance.
• Deploy and monitor products/services on cloud platforms in collaboration with Product Management and Business teams.
• Stay ahead of the curve in data persistence and big data technologies, running pilots to ensure scalability.
• Handle data integration, consolidation, and reconciliation for digital consumer and medical products.

Key Technical Skills:

SQL, AWS, Data Pipeline Architecture, ETL Development, AI & Data Science, Computer Vision, NLP, GenAI, Agentic Frameworks, Autoencoders, Transformers, LLMs, RAG Architecture, Deep Learning Models (Unet, YOLO), PyTorch, TensorFlow, OpenCV, Cloud Deployment, Statistics, Python

Requirements:

• Master’s or Ph.D. in Computer Science, Electrical/Electronics Engineering, or related field.
• Minimum 4 years in AI & Data Science.
• Understanding of modern computer vision, NLP, and Generative AI for imaging.
• Hands-on experience with deep learning frameworks and cloud-based AI deployment.
• Solid grasp of statistical analysis, regression, and testing.
• Strong analytical, problem-solving, and communication skills.