Job Description

Are you a MLOps and eager to consider a new challenge? How about a permanent job opportunity at a Data Analytics Company in Amsterdam or London?

General Information

• Duration: Intention of a permanent contract after 12 months
• No. of working hours: 36 hours per week
• Location: 1 to 2 times a week in the Amsterdam office
• Contract type: Employment / Direct Hire
• Salary: max 90.000 in Euro or GBP incl holiday allowance and 13th month

What is the Project About?

In this role, you will work at the intersection of Data Science and Engineering, helping transform experimental natural language processing (NLP), information retrieval (IR), and generative AI models into secure, reliable, and scalable production services.

The platforms process extremely large collections of structured and unstructured research data. You will contribute to the development of AI-driven capabilities such as generative AI applications, retrieval-augmented generation (RAG), intelligent search and ranking, recommendation systems, and knowledge graph–based retrieval, while ensuring compliance with data governance and content confidentiality requirements.

Key Responsibilities

• Automate and orchestrate machine learning workflows across major cloud and AI platforms.
• Maintain and manage model registries and artifact repositories to support reproducibility, versioning, and governance.
• Develop and maintain CI/CD pipelines for machine learning systems, including automated data validation, model testing, and deployment.
• Implement machine learning engineering solutions using widely adopted MLOps platforms and tooling.
• Build and maintain end-to-end pipelines for machine learning–driven recommendation systems.
• Design and develop engineering components for retrieval-augmented generation systems, including query interpretation, document chunking, embeddings, hybrid retrieval, and semantic search.
• Manage prompt libraries, guardrails, and structured outputs for large language model integrations.
• Design and implement machine learning pipelines that integrate search engines, vector databases, and graph databases.
• Develop evaluation pipelines using both traditional information retrieval metrics (e.g., NDCG, MAP, MRR) and LLM evaluation metrics such as factual grounding and response quality.
• Conduct controlled experiments and A/B testing to measure system improvements.
• Optimize infrastructure performance and cost through monitoring, scaling strategies, and efficient resource utilization.
• Stay current with advances in generative AI, natural language processing, and retrieval techniques, and apply relevant innovations to ongoing experimentation and system development.