Principal MLOps Engineer

Role overview

Principal MLOps Engineer (U.S. based)

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). You will help design, deploy, and mature Raft’s end-to-end ML platform and the MLOps infrastructure that supports model development, evaluation, deployment, monitoring, and lifecycle management across cloud and constrained environments.

Responsibilities

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Mature internal ML platform capabilities across the model lifecycle (packaging, registry/catalog workflows, deployment, monitoring, operational support)
• Deploy and manage ML workloads on Kubernetes, including GPU-enabled clusters
• Build/maintain model serving and inference infrastructure for multiple ML use cases (traditional ML, computer vision, speech/audio, and LLM-based systems)
• Create and operate CI/CD workflows for ML services, model artifacts, and platform components
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements (defense environment)
• Partner with ML engineers, software engineers, and product teams to move models from experimentation to reliable production deployment

Requirements

• 7+ years hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years experience with Docker and Kubernetes in production
• 5+ years experience supporting enterprise cloud infrastructure/applications in AWS, Azure, or similar environments

Nice-to-haves / additional signals

• Experience evaluating and standardizing deployment/serving runtime patterns for ML at scale
• Experience with secure production operations and compliance/supply-chain practices in regulated environments (defense-oriented)
• Familiarity with GPU infrastructure, model serving, and observability for ML systems

Location / eligibility

• U.S.-based role requiring U.S. citizenship and work performed within the continental U.S.