Machine Learning Ops Engineer

At Zone 5 Technologies, we're redefining what's possible in unmanned aircraft systems. Our team of engineers and innovators is developing cutting-edge autonomous solutions that push the boundaries of UAS technology - solving complex challenges that matter.

We're building the future of UAS capabilities, and we're looking for exceptional talent to join us. If you're driven by hard problems, energized by rapid innovation, and ready to make an impact on next-generation flight systems, you belong here.

We are investing in in-house LLM tooling and are hiring a dedicated MLOps Engineer to help grow it. You will build AI-powered capabilities—retrieval-augmented generation, tool integrations, and agentic workflows—and turn them into reliable services used by teams across the company. This is a builder's role focused on shipping new capability. 

The role spans a broad stack. We welcome both generalists and specialists—you do not need every skill listed below. Tell us where you are strong and where you want to grow. The center of gravity is LLM application development, retrieval quality, and agent design. 

Responsibilities: 

LLM Applications, RAG & Agents 

• Design and build new LLM-powered tools and agentic workflows that automate real work and improve productivity across the company 

• Extend and improve our RAG systems—ingestion, chunking, embedding, retrieval, ranking, and evaluation—to raise answer quality 

• Structure retrieval around the organization's information hierarchy so that relevance and access boundaries improve together 

• Build tool integrations that connect LLMs to internal systems and data sources 

• Design agents that act safely against real systems, with appropriate guardrails, human-in-the-loop where warranted, and clear failure behavior 

• Establish evaluation and testing frameworks to measure quality, catch regressions, and guide iteration 

• Partner with teams across the company to identify high-value use cases and turn them into deployed tools 

Service Deployment & AI Infrastructure 

• Deploy AI tools and services for teams across the company, taking them from prototype to reliable production 

• Build and operate the infrastructure that hosts models, tools, and supporting services on Kubernetes 

• Manage model serving, inference endpoints, and the APIs and gateways around them