Principal MLOps Engineer

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including 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 closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system