Senior AI Engineer in Cambridge

Work shouldn't just be something we do; it should have a purpose. At Envision, we believe in creating life-changing outcomes through the work we do with our clients, giving back to our communities, while creating a company culture where our people thrive. We believe success starts with a workplace where everyone feels valued, supported, and empowered to grow. Being part of a collaborative team means there's no limit to what you can achieve. With us, you can be a part of a growing company you want to work for.

Our Vision: To unleash the power of combined intelligence to accelerate patient access to life-changing treatments.

Our Mission: Delivering smarter and faster solutions to create, communicate, and commercialize value for our clients.

Our Values: Excellence, People, Growth

The opportunity

We are looking for a pragmatic Senior AI Engineer who will lead the end-to-end delivery of production-grade AI systems, combining strong software engineering with deep applied AI expertise. In this role, you will help set the technical direction for AI initiatives, own critical engineering efforts, and mentor engineers while collaborating cross-functionally to deliver AI systems that are reliable, measurable, and impactful in production. You will work closely with data scientists, software engineers, and product managers to build AI solutions that solve real problems for global pharmaceutical clients. We value engineers who prioritize evaluation over hype, automation where it adds leverage, and simple, resilient systems that perform well in real-world environments. This is a hybrid working opportunity from either our Cambridge, UK or Leiden, Netherlands office.

Role responsibilities

• Lead the technical strategy for AI initiatives spanning multiple teams and impacting global pharmaceutical clients.
• Design and deliver AI-powered applications from prototype through to production using modern LLM frameworks and APIs.
• Architect scalable, resilient, and observable AI systems, including RAG pipelines, agentic workflows, and context engineering strategies.
• Build and optimise Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and large language models.
• Apply agentic AI patterns (tool use, planning, reflection loops) pragmatically to solve complex problems.
• Develop context engineering strategies to improve model output quality and mitigate hallucination risks.
• Establish and evolve evaluation frameworks that measure real-world AI performance, including accuracy, latency, user experience, and safety.
• Champion engineering excellence through strong testing practices, code quality, observability, and CI/CD standards.
• Ensure AI systems meet security, compliance, and explainability standards within regulated environments.
• Collaborate closely with product, platform, and infrastructure teams to build systems that scale reliably and fail gracefully.
• Translate complex AI concepts for non-technical stakeholders and contribute to discovery, scoping, and roadmap planning.
• Mentor associate and mid-level engineers through code reviews, architecture guidance, and technical coaching.

Do you have what we are looking for?

Skills & expertise

• Extensive experience in software engineering or data science, with a strong demonstrated focus on ML or AI engineering.
• Expert-level proficiency in Python and experience integrating LLM APIs such as OpenAI, Anthropic, or similar platforms.
• Deep experience with GenAI frameworks including LangChain, LlamaIndex, Haystack, or Hugging Face Transformers.
• Strong experience implementing RAG pipelines using vector databases such as Pinecone, FAISS, or Weaviate.
• Proven ability to evaluate AI systems through custom evaluation frameworks, benchmarking, and dataset curation.
• Demonstrated experience delivering end-to-end AI applications, from concept and prototyping through production deployment and monitoring.
• Experience deploying agentic AI solutions in production environments with measurable business impact.
• Strong grounding in software engineering best practices, including version control, modular design, automated testing, and CI/CD.
• Excellent communication skills and ability to explain complex AI concepts to non-technical stakeholders.
• Strong understanding of AI ethics, bias mitigation, and responsible AI practices.

Nice to have

• Experience working with life sciences data, ontologies, or regulatory submissions.
• Familiarity with privacy-preserving techniques and model explainability in production AI systems.
• Experience with AWS, Kubernetes, feature stores, or model registries.
• Track record of optimising AI workloads for cost efficiency, including token usage, caching strategies, and hybrid retrieval.
• Experience building AI platforms, SDKs, or reusable AI pipelines, and managing external AI vendors.
• Exposure to clinical trial data or real-world evidence generation.

Do you have the experience we are looking for? If so, explore your place at Envision today!