Machine Learning Ops Engineer at Zone 5 Technologies

Job description

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 Operations 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 Qualifications: 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 Preferred: 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 Pay range for this role $140,000 — $175,000 USD What's in it for you: Benefits: Competitive total compensation package Comprehensive benefit package options include medical, dental, vision, life, and more. 401k with company-match 3 weeks of paid time off each year 40 hours of sick time 12 annual company holidays Why Join Zone 5 Technologies? Innovative Environment: Work on cutting-edge technology that is shaping the future of defense and aerospace. Collaborative Culture: Join a team of passionate professionals dedicated to pushing the boundaries of what’s possible. Career Growth: Opportunities for professional development and career advancement. If you are passionate about unmanned aircraft technology and want to be a part of a dynamic and growing company, we would love to hear from you. Apply today and join the Zone 5 Technologies team! In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire. Zone 5 Technologies is a federal contractor and participates in E-Verify to confirm employment eligibility. As required by law, we will verify the identity and employment authorization of all new employees using the E-Verify system. Learn more about your rights and responsibilities under E-Verify: e-verify.gov .

Requirements

Must-haves

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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmaikubernetesci/cdpythonml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Professional Development