This role builds scalable AI/ML infrastructure, data pipelines and agentic applications that enable scientists in pharmaceutical research and development.

Your tasks

Translate scientific and business needs into technical design and implementation of agentic AI solutions
Write, test and ship maintainable production code and build scalable systems and data pipelines
Apply software engineering best practices such as testing, CI/CD and documentation
Evaluate and scale ML systems using quantitative metrics and AI tooling
Contribute to architectural decisions, code reviews and evolution of development processes
Work across the stack beyond core area of expertise when needed

What you bring

Master's degree with 2+ years of professional experience or a PhD in Computer Science or similar technical field
Experience across the ML lifecycle: GPU optimization, model performance, backend systems, cloud-native architecture
Solid knowledge of statistics, ML theory and AI/ML frameworks, libraries and toolsets
Experience with software engineering best practices: testing, clean code, documentation
Strong communication skills across technical and non-technical audiences
Willingness to work onsite approximately 3 days a week

Conditions

Workload: 100 %
Work model: Hybrid
Option of a permanent position after the assignment if both sides want it

What to expect

Onsite work approximately 3 days a week
3–6 months interim, then a permanent position

Technologies

Kubernetes
AWS

How to apply

You apply with your Try&Hire profile: upload your CV, check the details, enter key facts and availability. A short introduction video is optional but helps. Our team will get in touch if your profile fits the assignment.

Machine Learning Engineer, AI Enablement