Are you an AI Engineer and eager to consider a new project ?
How about a role at a data analytics company in Amsterdam?
General information
Duration:Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 6-month contract with running extensions
No. of working hours:Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 36 hours per week
Location:Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â In the Amsterdam office on Tuesdays
Contract type:Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Payroll/ZZP
What is the project about?
We are seeking an experienced AI / LLM Engineer for a 6-month contract to help build next-generation AI-ready services in the medicinal chemistry and healthcare domain. You will play a key role in transforming complex chemistry and medicine APIs into intelligent, agent-driven systems that enable advanced data retrieval and decision-making.
Key Responsibilities
- Design and develop AI-ready services (e.g., MCPs, skills) on top of medicinal chemistry and healthcare APIs
- Implement agentic workflows to resolve ambiguity and orchestrate accurate data retrieval and processing
- Optimize and refine LLM prompts to improve performance, reliability, and output quality
- Build and maintain evaluation pipelines for training, testing, and benchmarking AI systems
- Collaborate closely with engineers, domain experts, and stakeholders to deliver production-ready solutions
- Ensure seamless integration of REST-based APIs into AI-driven architectures
Required Qualifications
- Proven experience with LLM prompt engineering and optimization
- Hands-on experience with agentic frameworks (observability tools are a strong plus)
- Strong understanding of data science workflows, including model evaluation and experimentation
- Solid experience working with HTTP-based REST APIs
- Ability to work in a collaborative, cross-functional environment
Nice to Have
- Experience in medicinal chemistry, life sciences, or healthcare data
- Familiarity with AI service architectures (e.g., MCP, tool/skill ecosystems)
- Experience with monitoring and observability in AI systems
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