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ML Engineering Lead (LLM Ops)

neko-healthStockholmEngineering
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Salary
Not listed
Job type
Full-time
Work mode
Remote
Experience
Lead
Posted
2 Oct 2026
Status
Open · checked 7d ago

About this role

Neko-health seeks an ML Engineering Lead for LLM Ops in Stockholm to manage the operational lifecycle of large language models and generative AI systems. This opportunity suits an experienced machine learning professional focused on building reliable production platforms for preventative healthcare applications.

Role Overview

Neko-health is hiring an ML Engineering Lead in Stockholm to oversee the deployment, monitoring, evaluation, and iteration of LLM, GenAI, and RAG-based systems. The position operates within the machine learning engineering area, collaborating closely with the existing MLOps team to support clinical workflows across regulated medical devices.

Key Responsibilities

Deploy MLflow Tracing observability across production LLM and agent pipelines to monitor prompts, tool calls, retrievals, latency, and cost.
Construct an evaluation framework utilizing custom and built-in LLM judges alongside human-feedback review loops.
Launch at least one RAG or agentic pipeline into production utilizing the MLflow Prompt Registry and Unity Catalog for versioning, rollouts, A/B testing, and rollbacks.
Formulate a documented framework for serving strategies evaluating third-party APIs, Databricks External Models, and self-hosted options regarding cost, latency, and GPU capacity.
Integrate LLM operations with the current MLOps team to maintain system reliability for clinical workflows.

Requirements

Strong MLOps background covering experiment tracking, training, and monitoring through independently managed production systems.
Proficiency in Python and foundational machine learning concepts with experience delivering end-to-end production systems.
Hands-on experience developing generative AI or LLM applications such as RAG, agents, chains, and prompt engineering using orchestration tools like LangGraph or LangChain.
Practical knowledge of distributed systems, PyTorch, and machine learning orchestration.
Theoretical understanding of LLM MLOps trade-offs including vector databases, embedding models, fine-tuning versus RAG, and human-feedback loops.
Demonstrable motivation focused specifically on LLM, GenAI, and RAG operations within complex environments like the medical sector.

Nice to Have

Background in AI-assisted or agentic coding workflows while maintaining complete accountability for the final output.
Familiarity with Terraform and Kubernetes for infrastructure management and self-hosted model serving.
Prior exposure to LLM safety scorers, guardrails, LLM-as-judge evaluation, tracing, and Databricks MLflow 3 for GenAI.
Ability to assess a rapidly changing tool ecosystem and provide context-specific recommendations.

Skills and Tools

Python
PyTorch
LangChain
LangGraph
MLflow
Unity Catalog
Kubernetes
Terraform
RAG
GenAI
LLMs

About the Company

Neko-health redefines preventative healthcare by combining proprietary technology with direct clinical care during single, non-invasive visits under an hour. The organization provides data-driven and personalized insights to help individuals sustain health before symptoms emerge.

How to Apply

Interested candidates should apply on the official company careers page.

Skills and tech stack

PythonPyTorchLangChainLangGraphMLflowUnity CatalogKubernetesTerraformVector DatabasesLLMRAGMLOpsFine-tuningPrompt EngineeringDistributed Systems

Questions candidates ask

Where is the ML Engineering Lead position located?

The role is located in Stockholm.

What core systems does this role manage?

The role manages the operational lifecycle of Neko-health's LLM, GenAI, and RAG-based systems, including deployment, monitoring, evaluation, and iteration.

Is hands-on model fine-tuning required for this position?

No, hands-on fine-tuning ownership is not required because execution for that area sits on a separate track.

What frameworks and tools are mentioned for LLM application development?

The posting lists LangChain, LangGraph, MLflow, Unity Catalog, Kubernetes, and Terraform.

Ready to apply?

The application is on the official neko-health careers page.

Apply on company site