ML Engineering Lead (LLM Ops)

The role

This role owns the operational lifecycle of Neko's LLM, GenAI and RAG based systems. You will build a production grade platform for clinical ML and GenAI workflows using proprietary sensor and device data within a regulated medical device QMS.

What you will do

• Stand up MLflow Tracing observability for production LLM and agent pipelines.
• Build an evaluation suite combining built-in and custom LLM judges and scorers.
• Ship at least one RAG or agentic pipeline to production.
• Produce a documented cost, latency and GPU capacity framework for serving strategy.
• Integrate LLM Ops with the existing MLOps team.

Required qualifications

• Solid MLOps fundamentals across the full lifecycle.
• Fluent in Python and core ML concepts.
• Track record of shipping end-to-end production ML systems and platformisation initiatives.
• Practical, hands-on experience building LLM or GenAI applications using frameworks such as LangChain, LangGraph, or comparable orchestration tools.
• Working knowledge of PyTorch, distributed systems and ML orchestration.
• Conceptual understanding of LLM-specific MLOps trade-offs: fine-tuning versus prompting versus RAG, vector databases, embedding models, and human-feedback loops.
• Demonstrable motivation for LLM, GenAI and RAG work.
• Ability to navigate complex systems spanning the medical domain, regulation, firmware and hardware.

Nice to have

• Experience with agentic or AI-assisted coding workflows.
• Kubernetes and Terraform.
• Exposure to LLM evaluation and observability practices: tracing, LLM-as-judge, guardrails and safety scorers.
• Databricks MLflow 3 for GenAI experience

About the company

Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. The mission is to make data-driven, preventative care accessible to more people, before symptoms appear.