Machine Learning Ops Engineer

Zone 5 Technologies develops autonomous unmanned aircraft systems and next-generation UAS capabilities. The MLOps Engineer will build and deploy LLM-powered applications, retrieval-augmented generation systems, agentic workflows, and reliable AI services, while operating supporting infrastructure, security controls, data pipelines, and production observability.

Responsibilities:

• 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
• 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
• 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
• Build CI/CD pipelines for AI tools, services, and agents
• Automate provisioning and configuration with Ansible and infrastructure-as-code practices
• Build data pipelines to ingest, transform, and index content for RAG and AI applications
• Manage vector databases and other stores backing retrieval and AI workloads, including versioning and quality checks
• Maintain reproducible environments across development, staging, and production

Requirements:

• Bachelor's in Computer Science, Software Engineering, Data Engineering, or related field – equivalent industry experience also welcome
• 3-6+ years of experience in MLOps, software, platform, or backend engineering (relevant depth matters more than exact years)
• Strong proficiency in Python and comfort building, shipping, and operating services
• Experience building LLM-powered applications—working with LLM APIs or self-hosted models, prompts, and tool/function calling
• Hands-on experience with Kubernetes and containerized deployment
• Solid understanding of CI/CD, infrastructure-as-code, and production service reliability
• Awareness of access control and data-boundary concerns when connecting tools to sensitive internal systems
• Demonstrated ability to learn quickly and work across unfamiliar parts of the stack
• Depth in at least one core area—LLM application development, RAG/retrieval, agent design, or AI infrastructure—with genuine interest in growing into the others
• Hands-on experience with RAG systems, embeddings, and vector databases (pgvector, Qdrant, Weaviate, Milvus, or similar)
• Experience designing and shipping agentic workflows, including tool use, orchestration, and guardrails
• Familiarity with the Model Context Protocol (MCP) or similar tool-integration frameworks for LLMs
• Experience integrating LLM tools with enterprise systems (productivity suites, business systems, or developer platforms) via their APIs
• Knowledge of LLM evaluation, prompt engineering, and quality/regression measurement
• Experience serving models and optimizing inference (vLLM, TGI, Triton, or similar)
• Familiarity with agent/orchestration libraries (LangChain, LlamaIndex, or equivalent)
• Experience with Ansible for configuration management and automation
• Experience implementing identity, authentication, and fine-grained authorization (OAuth, SSO, RBAC)
• Observability experience for AI/ML workloads, including usage and quality metrics
• GPU infrastructure and scheduling experience for training or inference
• Understanding of security and data-handling requirements in regulated or defense environments
• Ability to obtain or maintain a security clearance