GenAI Engineer (Series C Silicon Valley Startup, 70mil funding)

Our client is revolutionising healthcare for millions of patients worldwide. With over 20 years of proprietary wearable technology and clinically relevant signals, they are redefining primary care while helping people live happier, healthier, and longer lives. They are looking for a GenAI Engineer to join their team in London.

In this role, you will design, build, and optimise RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines, and health records. As a GenAI Engineer, your work will be at the heart of our client's mission to redefine healthcare. You'll be responsible for:

• Designing and implementing RAG architectures that leverage both open-source and potentially proprietary Large Language Models (LLMs).
• Building and maintaining retrieval pipelines over structured and unstructured health data.
• Developing indexing strategies using vector databases and embedding models.
• Integrating RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant.
• Working closely with product, clinical, and data science teams to fine-tune prompts, evaluate responses, and iterate on model performance.
• Building evaluation pipelines for factuality, relevance, and safety using synthetic and real-world datasets.
• Contributing to infrastructure for scalable GenAI deployments and model versioning.
• Staying up to date with the latest research in GenAI and health tech applications of LLMs.

What you bring:

• 3+ years of experience working in machine learning / NLP roles.
• Strong proficiency in Python, deep learning frameworks (PyTorch or TensorFlow), and GenAI libraries.
• Hands-on experience with vector search, embedding models, and retrieval pipelines.
• Familiarity with prompt engineering, prompt tuning, and evaluation of generative model outputs.
• Experience working with healthcare or sensitive data (HIPAA/GDPR compliance awareness).
• Strong problem-solving skills.
• Bonus: Experience with MLOps, Kubernetes, AWS/GCP, and deploying models in production.