ML Engineering

Mission

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

In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.

Role Purpose

This role owns the operational lifecycle of Neko's LLM, GenAI and RAG based systems: deployment, monitoring, evaluation and iteration. It builds the production grade platform that lets clinical ML and GenAI workflows run reliably on proprietary sensor and device data, inside a regulated medical device QMS, across Skin, Cardio and other use cases. The role sits within the ML Engineering Area and integrates tightly with the existing MLOps team rather than as a silo.

What You'll Deliver in the First 6-12 Months

● Stand up MLflow Tracing observability, prompts, tool calls, retrievals, latency and cost, live across production LLM and agent pipelines.

● Build an evaluation suite combining built-in and custom LLM judges and scorers, with a human-feedback loop via review apps, replacing today's ad hoc review.

● 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 rather than a separate track, protecting reliability on clinical workflows.

Minimum Qualifications

● Solid MLOps fundamentals across the full lifecycle, experiment tracking, training and monitoring, demonstrated through independent ownership of complex, production grade work.

● Fluent in Python and core ML concepts, with a track record of shipping end-to-end production ML systems and platformisation initiatives.

● Practical, hands-on experience building LLM or GenAI applications: prompt engineering, RAG, agents or chains, 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 for non-deterministic outputs. Hands-on fine-tuning ownership is not required, as execution sits with a separate track.