Lead ML Engineer – GenAI & LLM Ops Platform

Neko Health AB; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Neko Health is redefining prevention by combining proprietary sensors with clinical AI to deliver personalised insights in under an hour. This role owns the operational lifecycle of LLM, GenAI and RAG systems, working within the ML Engineering area to deploy and monitor production-grade AI workflows across medical use cases.

The role sits with the MLOps team, focusing on reliability, observability and scalable AI in a regulated medical domain, with opportunities to shape the platform and impact

Qualifications

• Solid MLOps fundamentals across the full lifecycle, including experiment tracking, training and monitoring.
• Fluent in Python and core ML concepts, with a track record of shipping production ML systems.
• Hands-on experience building LLM/GenAI applications: prompts, RAG, agents or chains.
• Knowledge of PyTorch, distributed systems and ML orchestration.
• Understanding of LLM-specific MLOps trade-offs and human-feedback loops for non-deterministic outputs.

Responsibilities

• Stand up MLflow Tracing observability across production LLM and agent pipelines.
• Build an evaluation suite combining built-in and custom LLM judges and scorers, with a human-feedback loop.
• Ship at least one RAG or agentic pipeline to production with prompt and application versioning through the MLflow Prompt Registry and Unity Catalog, enabling safe rollout, A/B testing and rollback.
• Produce a documented cost, latency and GPU capacity framework for choosing serving strategy: third party API versus Databricks External Models versus self-hosted.
• Integrate LLM Ops tightly with the existing MLOps team so it operates as a natural extension of the platform.

Skills

MLOps fundamentals

Python

Production ML systems

LLM/GenAI experience

RAG/agents/chains

Observability

Tools

LangChain

LangGraph

PyTorch

Kubernetes

Terraform

Databricks MLflow

Job description

Neko Health is redefining prevention by combining proprietary sensors with clinical AI to deliver personalised insights in under an hour. This role owns the operational lifecycle of LLM, GenAI and RAG systems, working within the ML Engineering area to deploy and monitor production-grade AI workflows across medical use cases.

The role sits with the MLOps team, focusing on reliability, observability and scalable AI in a regulated medical domain, with opportunities to shape the platform and impact