Remote C# Engineer

We're looking for AI engineers who want to build production-ready AI solutions that tackle real-world challenges in life sciences, from RAG chatbots to agentic AI systems supporting evidence-driven projects. You will design, develop, and deploy AI systems that have measurable impact, influence patient access to treatments, and support decision-making in healthcare. Our client is a fast-growing life sciences technology company with offices in the UK and Europe. They specialise in applying AI to accelerate patient access to treatments through practical, evidence-driven solutions. The company values collaboration, innovation, and creating a culture where employees can grow and make a tangible impact in healthcare. Design, develop, and deploy AI systems with a focus on RAG chatbots and agentic AI. Lead real-world evidence projects, ensuring AI solutions are reliable, measurable, and impactful. Implement data pipelines, retraining workflows, and monitoring to maintain model performance. Design evaluation metrics to assess accuracy, latency, UX quality, safety, and real-world utility. Collaborate with product, software, and platform teams to deliver scalable, production-ready AI solutions. Champion software engineering best practices, including code reviews, testing, CI/CD, and reproducibility. Ensure AI solutions meet security, compliance, and responsible AI standards.

Mid-to-senior AI engineer with 3+ years in data science or software engineering, including substantial AI experience. Strong Python skills and experience with LLM APIs (OpenAI, Anthropic, or similar). Hands-on experience with RAG pipelines, vector databases (Pinecone, FAISS, Weaviate), and chatbot deployment. Proven experience with agentic AI (tool use, planning, multi-step reasoning) and production-level systems. Experience delivering AI solutions in real-world or evidence-driven projects, preferably in life sciences. Strong communication skills and understanding of AI ethics, bias mitigation, and responsible AI practices.

Model fine-tuning, knowledge graphs, or multi-modal AI systems. AWS or other cloud platform experience for scalable GenAI deployment. Client-facing experience in AI projects.

Hybrid working model (UK & Europe).