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As our next AI Engineer, you will contribute to solving meaningful challenges across the life sciences industry. We offer specialised management consulting services, working with top-tier life sciences companies as well as biotech startups — locally and globally. From strategy development to hands-on implementation, we support our clients were impact matters most. About the role Generative and agentic AI has moved from demos to production. With this, one of our key focus areas is building products with an agentic AI engine at their core that are reliable, secure, auditable, and good enough to put in front of a medical writer or a regulatory reviewer. That challenge has two halves: The AI engine: Orchestration, retrieval, context engineering, tool use, guardrails, and the evals and observability that make an agentic system able to solve business-critical tasks in a regulated environment. The product around it: The UI, APIs, services, data layers, integrations, and deployment that turn that engine into robust, secure, scalable software customers depend on. Today these two halves often pull against each other. We are building a team where they pull together. As an AI Engineer, you will focus on the AI engine itself: designing, building, evaluating, and improving agentic systems that solve real business problems in highly regulated environments.
Job Responsibility
Design, build, and ship AI-powered tools that help life science teams create, review, and manage critical documents and data
Build features such as grounded retrieval, summarisation, document comparison, question answering, and content classification with traceability back to source
Design agentic workflows capable of handling complex business processes
Build evaluation frameworks and observability capabilities that ensure reliability and quality
Ensure solutions are secure, auditable, and scalable in regulated cloud environments (AWS, Azure, GCP)
Collaborate closely with clinical, regulatory, quality, and commercial experts to understand their workflows and implement AI solutions that create measurable value
Requirements
Strong Python skills and solid software engineering fundamentals
Hands-on experience with at least one major cloud (AWS, Azure, or GCP)
Deep, hands-on experience with enterprise LLMs and their APIs (e.g. Anthropic/Claude, OpenAI, Google), including tool/function calling, structured outputs, and the Model Context Protocol (MCP)
Experience building agentic systems with modern orchestration frameworks such as Pydantic AI, LangGraph, OpenAI Agents SDK, AutoGen / Microsoft Agent Framework, or LlamaIndex
Strong command of retrieval techniques including RAG and graph-RAG, vector search, embeddings, reranking, and document-processing pipelines
Practical context-engineering skills and the ability to consistently improve AI quality through prompt and context design
Experience with evals and observability as first-class concerns, including evaluation suites, tracing, regression testing, reliability, latency, and cost management
Familiarity with tooling such as LangSmith, Langfuse, Braintrust, Arize Phoenix, MLflow, DeepEval, or Promptfoo
A solid foundation in data science fundamentals, data analysis, and statistical thinking
Strong communication skills in English
Nice to have
Understanding of pharmaceutical clinical and regulatory documents and workflows
Awareness of GxP, 21 CFR Part 11, GDPR, HIPAA, and computer system validation
Experience with semantic computing and document structuring
Experience deploying secure, compliant applications in regulated cloud environments
Experience with life science upstream source systems such as Veeva
What we offer
Support for health and wellbeing, covering physical, mental, and social needs
Flexible ways of working, built on trust, autonomy, and balance
Ongoing learning and professional development throughout your career
A modern work setup, with the tools and equipment needed to do great work
Recognition of performance and impact, linked to contribution and results
The opportunity to work on challenges that make a meaningful difference for patients and healthcare systems worldwide