Machine Learning Operations (MLOps) Engineer

Sourced from feeny.ai

Gallatin builds AI-driven logistics decision systems for U.S. national security and allied missions. As an MLOps Engineer, you will own the path from training run to deployed capability: training and serving infrastructure, release and reproducibility processes, evaluation harnesses, and production telemetry. The role spans AWS and on-premises GPU environments, including air-gapped and accredited IL5/IL6 enclaves.

• Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware
• Stand up and tune LLM inference serving (vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing)
• Build CI/CD for models and pipelines with versioned datasets, model registry, promotion gates, and rollback
• Own infrastructure as code, containerization, and GitOps deployment across environments including disconnected enclaves
• Build and own evaluation harnesses: regression suites, LLM-as-judge pipelines, adversarial and held-out sets
• Instrument production for drift, latency, cost, retrieval quality, and failure modes
• Own ingestion, versioning, and lineage for logistics and doctrinal data
• Build embedding and feature pipelines, incremental indexing, and human-in-the-loop infrastructure
• Deploy and operate ML systems in IL5/IL6 environments and support ATO and continuous-authorization work

• 5+ years in MLOps, ML platform, or infrastructure engineering
• Strong Python skills in production codebases
• Deep Kubernetes and containerization experience plus infrastructure as code
• Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent)
• Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost
• Experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments
• Experience shipping an LLM or ML system to production and maintaining it
• Experience building evaluation and monitoring for ML systems
• U.S. citizenship required; ability to obtain and maintain a security clearance

• LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching)
• Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, citation quality
• Edge, on-premises, or disconnected deployment experience
• Experience supporting ATO, continuous authorization, or classified-environment operations
• Defense, intelligence, or other accredited/regulated environment experience
• Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD
• Degree in CS, engineering, or a related technical field