ML Engineering Lead (LLM Ops)
Lead the operational lifecycle of Neko's LLM and GenAI systems, ensuring reliable clinical ML workflows.
Posted by employer 2 days ago
First seen on Joblaze 1 day ago
Last verified on the company career page 1 day ago
Skills & Technologies
What you'll build
- Stand up MLflow Tracing observability
- Build an evaluation suite for LLM judges and scorers
- Ship RAG or agentic pipeline to production
- Produce a documented cost, latency and GPU capacity framework
- Integrate LLM Ops with existing MLOps team
Must have
- Solid MLOps fundamentals across the full lifecycle
- Fluent in Python and core ML concepts
- Practical experience building LLM or GenAI applications
- Working knowledge of PyTorch and distributed systems
- Conceptual understanding of LLM-specific MLOps trade-offs
- Genuine motivation for LLM, GenAI and RAG work
Nice to have
- Experience with agentic or AI-assisted coding workflows
- Kubernetes and Terraform experience
- Exposure to LLM evaluation and observability practices
- Comfort navigating a fast-moving tools and platform ecosystem
AI in the day-to-day
This role involves building and integrating LLM and GenAI workflows into clinical ML systems.
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
The ML Engineering Lead at Neko Health is responsible for managing the operational lifecycle of large language models and generative AI systems, focusing on deployment, monitoring, and iteration. This role requires strong MLOps expertise, proficiency in Python, and hands-on experience with LLM applications, particularly in a regulated medical environment. It is well-suited for someone with a solid background in production-grade ML systems and a specific interest in LLM and GenAI technologies. The position emphasizes collaboration with the existing MLOps team to ensure seamless integration and reliability in clinical workflows.
Joblaze insights
- Listed yesterday — first seen on Joblaze October 3, 2026. Last confirmed on Neko Health's careers page October 3, 2026.
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Databricks, Kubernetes, LangChain, LangGraph, MLflow, PyTorch.
- What seniority level is this role?
- Neko Health targets lead candidates for this position.
- Is this full-time or contract?
- Full-time for this ML Engineering Lead (LLM Ops) role at Neko Health.
From the original posting
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, reflecting Neko's Tech-Enabled, Human-Centred vital in practice.
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 the candidate retains 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.
Standard company text repeated across Neko Health's postings is omitted here.