Platform Engineer - AI/ML Infrastructure

Platform Engineer, AI/ML Infrastructure

Location:  U.S - Remote OR Hybrid - Washington, DC, Denver, CO or Colorado Springs, CO. 

Work Authorization: U.S. citizenship required

Clearance:  U.S.-Remote Opening: An active clearance is not required. Candidates must be eligible and willing to obtain and maintain a U.S. security clearance. Hybrid Opening: An active TS/SCI clearance is preferred. Candidates may also be considered if they previously held a TS/SCI with CI polygraph or currently hold an active TS or Secret clearance.

Salary Range: $120,000–$250,000 USD, dependent on experience level and location

Openings: Two positions are available:

• One hybrid position: Candidates must be located in or willing to work hybrid from Washington, DC; Denver, CO; or Colorado Springs, CO. An active TS/SCI clearance is required. Up to 15% travel is required.
• One U.S.-remote position: Candidates may work remotely from anywhere in the United States. An active clearance is not required, but candidates must be willing and able to undergo the process required to obtain and maintain a U.S. security clearance.

Candidates will be considered for the opening that best aligns with their location, clearance status, experience, and work preferences. Candidates who meet the requirements for multiple openings may be considered for more than one.

About the Role

OpenTeams builds AI platforms that governments and enterprises own outright: the infrastructure, the data, the models, and the evidence that the whole thing does what it claims. We're hiring several engineers to build and run that infrastructure.

The work spans the full depth of an AI platform. The Kubernetes clusters that schedule GPU workloads, move large datasets, and keep tenants isolated from one another. The services that make it a platform rather than a cluster: workflow orchestration, data ingest, model serving, policy enforcement, audit logging. The delivery path that gets released into production reliably and can prove what it shipped. The cloud infrastructure that ties it all together, and the operational practices that keep a distributed system resilient.

Much of this has to run where you can't assume normal cloud resources, or even an internet connection. That constraint is the interesting part of the job. Portability, reproducibility, and operability are design inputs from the first commit rather than problems handed downstream.

We build on open source and contribute back. Kubernetes, Terraform and OpenTofu, Argo, Prometheus, Nebari, and others. Upstream work is part of the job, not something you do on weekends.

This posting covers multiple roles, spanning mid-level through senior. We understand nobody spans every area above, so tell us where you fit. We assign level-based roles based on what you've actually done rather than a year count.

Key Responsibilities

• Build and operate Kubernetes-based infrastructure for demanding AI/ML workloads, including GPU scheduling, resource management, and multi-tenant isolation
• Design and implement platform services for orchestration, data ingest, model serving, and results management behind documented APIs
• Write infrastructure as code and build GitOps pipelines so environments are reproducible from source
• Build and operate CI/CD pipelines that produce versioned, signed, scanned release artifacts along with the documentation needed to deploy them
• Own reliability: capacity planning, upgrade paths, failure-mode analysis, backup and recovery, incident response, and postmortems
• Implement monitoring, logging, tracing, and alerting, and define the service level objectives they're measured against
• Deploy and validate the platform in restricted, disconnected, or limited-connectivity environments, and verify parity after each release
• Keep the platform portable by constraining dependencies to what's confirmed available in target environments
• Write runbooks and operational documentation that other engineers can execute without you in the room
• Contribute to Nebari and other open-source infrastructure, Kubernetes, and MLOps projects
• Work with security engineers, government stakeholders, and other engineers to turn requirements into systems that hold up
• Collaborate asynchronously across a distributed team

Required Skills & Experience

• U.S. citizenship, and the ability to obtain and maintain a U.S. security clearance
• Four or more years of hands-on experience building or operating production infrastructure, platforms, or distributed systems
• Production experience with Kubernetes and containerized workloads
• Experience with at least one major cloud platform: AWS, Azure, or Google Cloud
• Experience with infrastructure as code and CI/CD, using tools such as Terraform, OpenTofu, Pulumi, or Helm
• Working proficiency in Python, Go, Bash, or a comparable language
• Experience implementing or operating production monitoring and observability
• Ability to write documentation, runbooks, and deployment procedures that other people can actually follow
• Ability to work independently and collaborate well in a remote, distributed team

Nice to Have

You will not have all of these, and very few people will. They're the things that would help, not a checklist. If the required list above describes you, apply.

• An active U.S. security clearance, particularly TS/SCI with CI polygraph
• Experience deploying or operating software in air-gapped, disconnected, or otherwise restricted environments
• Experience with Department of Defense, Intelligence Community, or comparably regulated programs
• Familiarity with the Risk Management Framework, NIST 800-53 or 800-171, or similar frameworks, and with producing the evidence they require
• Experience supporting an Authorization to Operate, or with continuous ATO models
• Supply chain security work: hardened images, artifact signing, SBOM generation, dependency and container scanning, policy enforcement
• Familiarity with cross-domain solutions, guards, data diodes, or similar transfer mechanisms
• Experience with classified cloud environments, including AWS Secret or Top Secret regions
• A DoD 8140/8570 qualifying certification such as Security+, CISSP, CASP+, or CISM, or willingness to obtain one after joining
• Experience building MLOps pipelines or infrastructure for AI/ML workloads
• Experience with GPU scheduling, distributed inference, or large-scale data and evaluation pipelines
• Experience with model-serving or gateway frameworks such as KServe, vLLM, or LLM-D
• Experience designing API-first services and vendor-agnostic platforms that run across multiple environments
• Experience with agentic workflow frameworks or multi-step AI pipeline orchestration
• Contributions to open-source Kubernetes, infrastructure, MLOps, or observability projects, and experience with Nebari specifically
• Familiarity with data sovereignty and privacy requirements for enterprise or government AI systems
• Experience leading technical initiatives, setting engineering standards, or mentoring other engineers
• Experience supporting rapid prototyping programs or defense innovation initiatives