MLOps / LLMOps Engineer

Build the infrastructure that keeps ML and LLM systems observable, testable, deployable, and trustworthy. Impact Create repeatable paths from experiment to production. Improve AI reliability through evaluation, monitoring, and deployment discipline. Support teams building models, retrieval systems, and agents. What you'll do Build model deployment, evaluation, prompt/version management, and monitoring workflows. Automate CI/CD for AI services and data-dependent applications. Implement observability for model quality, cost, latency, drift, and failures. Support secure integration with model providers and cloud AI services. What we're looking for 4+ years of platform, ML infrastructure, DevOps, or backend engineering experience. Experience with containers, CI/CD, observability, and production services. Familiarity with model deployment, LLM evaluation, or data pipelines. U.S. Citizenship is required. Must be capable of obtaining and maintaining a Top Secret security clearance. Comfort working with ambiguous mission, data, and engineering problems. Strong written communication and ability to explain technical decisions to technical and non-technical teams. Preferred experience AWS experience is a plus, especially secure cloud, GovCloud, serverless, data, or AI services. AI, machine learning, LLM, agentic workflow, or decision intelligence experience is a plus. Experience in defense, government, manufacturing, aerospace, logistics, or other mission-critical environments is a plus. Hiring note Anautics evaluates candidates based on role-relevant skills, judgment, ownership, and ability to work in high-trust environments. AWS and AI experience are strong pluses across this hiring plan. #J-18808-Ljbffr ANAUTICS INC