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. The team is investing in a more mature end-to-end machine learning platform to support 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 Raft’s internal ML platform and model lifecycle capabilities (packaging, model registry/catalog workflows, deployment, monitoring, and operational support)
• Deploy and manage ML workloads on Kubernetes, including GPU-enabled clusters
• Support model serving/inference infrastructure across ML use cases (traditional ML, computer vision, speech/audio, and LLM-based systems)
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner with ML engineers, software engineers, and product teams to move models from experimentation to reliable production
• Improve observability, reliability, security, and maintainability for ML infrastructure and services
• Evaluate and 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

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

Notes / Eligibility

• This is a U.S.-based position requiring U.S. citizenship
• Work must be conducted within the continental U.S.