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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.
• Genuine, demonstrable motivation for LLM, GenAI and RAG work specifically, not generic MLOps, and the ability to navigate complex systems spanning the medical domain, regulation, firmware and hardware. Preferred Qualifications
• Experience with agentic or AI-assisted coding workflows, provided you retain full ownership and understanding of the resulting output.
• Kubernetes and Terraform, useful for infrastructure as code or self-hosting fine-tuned or open-source models outside managed serving.
• Exposure to LLM evaluation and observability practices: tracing, LLM-as-judge, guardrails and safety scorers. Databricks MLflow 3 for GenAI experience is a strong plus.
• Comfort navigating a fast-moving tools and platform ecosystem, and distilling recommendations relevant to Neko s specific context.

Zur Position

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.