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  Implement monitoring, logging, and usage observability so we understand how tools perform and get used  Access, Security & Data Boundaries  Ensure retrieval and agent tools respect the same access boundaries as the underlying systems—no cross-team or cross-project data leakage  Integrate with existing identity and permission systems so tools honor who is allowed to see what  Apply data-handling practices appropriate to a defense environment  Treat access control as a first-class design concern in every tool, not an afterthought  Automation & Data Operation...