AI Developer

Merck Merck · Pharma · Central Bohemian, Czech Republic

This role focuses on the design and delivery of enterprise-grade AI solutions, specifically generative AI and RAG-based components. The individual contributor will implement, integrate, and optimize AI systems, working with LLMs, vector databases, and retrieval components. The role emphasizes hands-on development, collaboration with cross-functional teams, and adherence to engineering best practices within an Agile framework.

What you'd actually do

  1. Implement and improve AI systems, including generative AI and RAG‑based components
  2. Own well‑defined development tasks within larger AI workstreams
  3. Write high‑quality, maintainable code and follow engineering best practices
  4. Integrate language models, vector databases, retrieval components, and evaluation tools under guidance from senior engineers
  5. Contribute to model optimization for performance, reliability, and responsible AI use

Skills

Required

  • Python
  • AI/ML pipelines
  • cloud services
  • data pipelines
  • databases
  • APIs
  • modern engineering tooling
  • RAG systems
  • LLMs
  • vector databases
  • prompt engineering
  • generative AI applications
  • ML fundamentals
  • CI/CD
  • testing practices
  • MLOps concepts
  • RESTful APIs

Nice to have

  • AWS
  • Agile environments
  • containerization (Docker)
  • orchestration tools
  • prompt optimization
  • evaluation frameworks for generative AI

What the JD emphasized

  • production-grade AI or ML systems
  • Hands-on exposure to RAG systems, LLMs, vector databases, prompt engineering, or generative AI applications

Other signals

  • Implement and improve AI systems, including generative AI and RAG-based components
  • Integrate language models, vector databases, retrieval components, and evaluation tools
  • Contribute to model optimization for performance, reliability, and responsible AI use
  • Participate in experimentation and iteration of RAG pipelines, prompt strategies, and model-driven workflows
  • Experience contributing to production-grade AI or ML systems
  • Hands-on exposure to RAG systems, LLMs, vector databases, prompt engineering, or generative AI applications