AI/ML & Prompt Engineer LLM, RAG & Voice Agent
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Mid
- Apply from
- United Kingdom
About the role
- Own the intelligence behind EMMA's AI systems.
- Design and optimise AI models for patient interactions.
- Collaborate with frontend and backend teams.
- Implement continuous-learning improvements.
- Mentor junior engineers in MLOps and prompt engineering.
Skills the posting asks for
- AI
- Python
- MLOps
- LLM
- RAG
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
ABOUT QUANTUMLOOPAI
QuantumLoopAI is transforming how patients access healthcare. Our AI-powered reception system, EMMA, serves millions of patients, helping GP surgeries manage demand, reduce phone queues, and ensure patients get the right care at the right time. Backed by leading healthcare investors and working with stakeholders across England, we are building the future of primary care access. As we prepare for our Series A and expand our team, we are looking for exceptional people who want to make a genuine difference to healthcare.
ABOUT THE ROLE
As our AI/ML & Prompt Engineer, you will own the intelligence behind EMMA — the LLM and RAG systems that power our chatbot, voice agent and telephony services. You will design, build and optimise the AI models and prompt architectures that allow EMMA to understand patient needs, retrieve accurate clinical information, and respond naturally across text and voice channels. This is a hands-on, high-impact role where your work will directly shape patient outcomes across hundreds of GP surgeries.
WHAT YOU WILL DO
- Architect and deploy LLM- and RAG-based systems that ingest data sources, generate accurate responses, and power both chatbot and voice agent interactions.
- Design, iterate and optimise prompts and voice-agent dialogue flows to maximise response relevance, reduce latency and ensure clinical safety across text and voice channels.
- Partner with frontend (Next.js, React) and backend (NestJS, Python) teams to integrate AI and voice agent components into our Azure-hosted microservices architecture.
- Instrument AI and voice-agent pipelines, analyse logs and user feedback, troubleshoot edge cases, and implement continuous-learning improvements.
- Stay abreast of the latest advances in LLMs, RAG, conversational AI frameworks, and (GDPR, DTAC) to inform our technical roadmap.
- Define and execute rigorous test plans in collaboration with QA to validate model accuracy, voice-agent performance and compliance with healthcare standards.
- Mentor junior engineers and advocate for best practices in MLOps, prompt engineering, voice-agent design and model governance.