Machine Learning Ops Engineer
Core
Building in-house LLM tooling including retrieval-augmented generation, tool integrations, and agentic workflows to serve internal teams.
Role type
MLOps Engineer (LLM Infrastructure & Application)
Builds
AI-powered services, RAG systems, and agent workflows deployed on Kubernetes.
Domain
Artificial Intelligence / Large Language Models / Unmanned Aircraft Systems
Required skills
LLM application development, RAG system design, agent design, Kubernetes, model serving, API gateway management, observability, access control integration
Preferred skills
None stated
Technologies
Kubernetes, LLMs, RAG frameworks
Responsibilities
Design and build LLM-powered tools and agentic workflows; extend and improve RAG systems for retrieval quality; build tool integrations connecting LLMs to internal systems; design safe agents with guardrails; establish evaluation and testing frameworks; deploy AI tools from prototype to production; build and operate Kubernetes infrastructure for models and services; manage model serving and inference endpoints; implement monitoring and usage observability; ensure tools respect access boundaries and integrate with identity systems
