Principal MLOps Engineer

Raft Company Website•San Antonio, CO

1d•$150,000 - $200,000•Remote

About The Position

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making. Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments. In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

Requirements

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

Nice To Haves

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

Responsibilities

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