Top Tech Jobs & Startup Jobs

Build and operate Kubernetes-based AI/ML infrastructure, including GPU scheduling, multi-tenant isolation, platform services, infrastructure as code, GitOps, CI/CD, observability, and reliability practices. Deploy reproducible platforms in cloud, restricted, and disconnected environments. Develop runbooks, support incident response, contribute to open-source infrastructure projects, and collaborate with security engineers and government stakeholders. The role requires production infrastructure experience, cloud expertise, programming skills, and eligibility for a U.S. security clearance.

Build API-first backend services for workflow orchestration, data ingestion, evaluation, and results management, along with analyst-facing web consoles. Implement identity federation, authentication, authorization, relational data models, automated testing, and CI pipelines. Create API documentation, SDK examples, reference implementations, notebooks, and integration resources. Collaborate with security, infrastructure, platform, and ML engineers while supporting external partners and Government users integrating with published interfaces.

Own the end-to-end architecture of a secure AI testing and evaluation platform for Government customers. Define technical roadmaps, interfaces, standards, and design patterns; evaluate open-source and commercial technologies; guide security, interoperability, scalability, and reliability decisions; and support deployment in restricted environments. Advise stakeholders, coordinate multiple engineering disciplines, document technical risks and tradeoffs, support proposals, and mentor engineers and technical leads.

Supports internal and customer infrastructure platforms through incident triage, systems administration, cloud provisioning, Kubernetes deployments, identity management, monitoring, security hygiene, and runbook execution. The role requires Linux, networking, scripting, Git, cloud, container, and SaaS administration skills, with opportunities to grow into environment builds, configuration changes, compliance work, and platform ownership. It is fully remote, U.S.-based, and supports government-related environments requiring Secret clearance eligibility.

Lead technical project success for enterprise AI infrastructure engagements. Collaborate with engineers, clients, and account leaders to capture requirements, decisions, dependencies, risks, and tradeoffs. Track constraints involving security, compliance, data sovereignty, procurement, and restricted environments. Maintain project visibility, align deliverables with business objectives, resolve issues, support implementation-to-operations handoffs, and identify expansion and renewal opportunities. Translate complex technical concepts for engineering, business, and executive stakeholders.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Lead design and delivery of large-scale open-source AI solutions: translate business needs into architectures, advise on ML frameworks and MLOps, support pre-sales, engage clients and open-source communities, mitigate technical risks, and guide development teams to production.