n
nPrincipal MLOps Engineern
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nRemote, US; DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HIn
n
nn This is a U.S. based position. All of the programs we support requiren nn U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S.n
n Who we are:n
Raft (n n n ) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.
We’re looking for an experiencedn n Principal ML Ops Engineern n to support our customers and join our passionate team of high-impact problem solvers.
n About the role:n
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.
What you’ll do:n
- n Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systemsn
- n Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational supportn
- n Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clustersn
- n Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systemsn
- n Build and maintain CI/CD workflows for ML services, model artifacts, and platform componentsn
- n Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deploymentn
- n Improve observability, reliability, security, and maintainability across ML infrastructure and servicesn
- n Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloadsn
- n Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environmentsn
- n Support compliance-driven deployment practices and secure software supply chain requirements in defense environmentsn
- n Get hands-on with customers at the most forward-leaning places in the Department of Warn
n What we are looking for:n
- n 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical rolesn
- n 5+ years of experience with Docker and Kubernetes in production environmentsn
- n 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environmentsn
- n Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in productionn
- n Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teamsn
- n Practical experience deploying machine learning workloads on Kubernetesn
- n Experience managing clusters or workloads that use GPUsn
- n Strong understanding of Helm and Kubernetes deployment patternsn
- n Strong scripting or programming skills, preferably in Pythonn
- n Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflowsn
- n Strong troubleshooting, systems thinking, and communication skillsn
- n Ability to work independently and collaboratively in a fast-moving environmentn
- n Ability to obtain and maintain a Top Secret clearancen
- n Ability to obtain Security+ certification within the first 90 days of employmentn
n Highly preferred:n
- n Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologiesn
- n Experience with secure and compliant deployment practices in regulated or government environmentsn
- n Experience with Kubernetes-based ML platforms such as Kubeflown
- n Familiarity with service mesh technologies such as Istion
- n Experience provisioning and debugging complex CI/CD systemsn
- n Experience with infrastructure as code tools such as Terraformn
- n Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanningn
- n Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edgen
- n Background working with machine learning engineers on model training, evaluation, packaging, and release workflowsn
- n Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog systemn
n What success looks like:n
- n You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in productionn
- n ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to usen
- n Model deployments become more secure, observable, and supportable across real-world mission environmentsn
- n The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownershipn
n Clearance Requirements:n
- n Ability to obtain and maintain a Top Secret clearancen
n Work Type:n
- n Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HIn n Locations ONLYn
- n May require up to 40% traveln
n Salary Range: $150,000.00 - $200,000.00n
n What we will offer you:n
- n Highly competitive salaryn
- n Fully covered healthcare, dental, and vision coveragen
- n 401(k) and company matchn
- n Take as you need PTO + 11 paid holidaysn
- n Education & training benefitsn
- n Annual budget for your tech/gadgets needsn
- n Monthly box of yummy snacks to eat while doing meaningful workn
- n Remote, hybrid, and flexible work optionsn
- n Team off-site in fun places!n
- n Generous Referral Bonusesn
- n And More!n
n Our Vision Statement:n
We bridge the gap between humans and data through radical transparency and our obsession with the mission.
n Our Customer Obsession:n
We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.
n How do we get there?n
Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm.
Raft’s core philosophy isn n Ubuntu: I Am, Because We aren n . We support ourn n “nadi”n n by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration.n
n n We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.n n
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