GenAI Engineer – Retrieval-Augmented Generation (RAG)

About the Role

• You will design, build, and optimize 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. Your work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing.

Responsibilities

• Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic).
• Build and maintain retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation).
• Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT).
• Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant.
• Work closely with product, clinical, and data science teams to fine-tune prompts, evaluate responses, and iterate on model performance.
• Build evaluation pipelines for factuality, relevance, and safety using synthetic and real-world datasets.
• Contribute to infrastructure for scalable GenAI deployments and model versioning.
• Stay up to date with the latest research in GenAI and health tech applications of LLMs.

Requirements

• 3+ years of experience working in machine learning / NLP roles, with recent focus on LLMs and/or GenAI.
• Strong proficiency in Python, deep learning frameworks (PyTorch or TensorFlow), and GenAI libraries (LangChain, LlamaIndex, Transformers).
• 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 and ability to move fast in a startup environment.
• Bonus: Experience with MLOps, Kubernetes, AWS/GCP, and deploying models in production.

Work Permit

• UK work permit required

Education

• Masters Degree