Principal MLOps Engineer

Role Overview

You will be a Principal MLOps Engineer helping design, deploy, and mature Raft’s end-to-end machine learning platform and MLOps infrastructure. Raft builds mission-critical AI/data platforms for the DoD, including low-latency pipelines, model lifecycle management, and secure production operations across cloud and constrained environments.

What You’ll Do

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems.
• Mature internal ML platform and model lifecycle capabilities (model packaging, registry/catalog workflows, deployments, monitoring, operational support).
• Deploy and manage ML workloads on Kubernetes, including GPU-enabled clusters.
• Build and operate model serving/inference infrastructure for multiple ML types:
  • traditional ML, computer vision, speech/audio, and LLM-based systems
• Create 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 across ML infrastructure.
• Evaluate and standardize runtime patterns, serving frameworks, and deployment architectures.
• Contribute to infrastructure decisions across edge, on-prem, and cloud environments.
• Support compliance-driven deployment practices and secure software supply chain requirements (defense environment).

What You’re Looking For

• 7+ years hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related roles.
• 5+ years experience with Docker and Kubernetes in production environments.
• 5+ years experience supporting enterprise cloud infrastructure/applications on AWS, Azure, or similar.
• Strong experience provisioning and operating production infrastructure for ML systems (the posting continues beyond the excerpt).

Preferred/Implied Fit

• Deep understanding of both production ML infrastructure and the practical needs of ML engineers.
• Experience with secure production operations, monitoring/observability, and model lifecycle practices for AI/ML at scale.

Security/Eligibility

• U.S. citizenship required and work must be conducted within the continental U.S.