Jobs at OpenTeams

Build and operate containerized, API-first AI platform services for workflow orchestration, data ingestion, model serving, policy enforcement, audit logging, and usage accounting. Deploy and validate releases across cloud, dedicated, classified, air-gapped, and limited-connectivity environments. Design for portability, reliability, scalability, and performance; conduct load testing, verify environment parity, integrate data sources, and resolve high-side defects through sanitized feedback paths.

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.

Owns end-to-end execution of an AI/ML platform program supporting national security missions. Responsibilities include managing cost, schedule, performance, milestones, contract deliverables, risks, changes, Government dependencies, subcontractors, and stakeholder relationships. The role leads program reviews, coordinates security onboarding and classified work, manages contractual requirements, and translates technical progress into executive and Government reporting.

Build and operate secure CI/CD pipelines for AI/ML platforms in regulated and classified environments. Responsibilities include producing signed release packages and SBOMs, promoting software through controlled or air-gapped environments, integrating vulnerability management and compliance evidence, operating Kubernetes and AWS workloads, implementing observability, managing incidents, and maintaining deployment documentation. The role requires collaboration with security teams and support for RMF, accreditation, and continuous monitoring requirements.

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.

Build and operate a secure Kubernetes-based AI/ML platform for government test and evaluation teams. Responsibilities include GPU scheduling, workload orchestration, infrastructure-as-code, GitOps, observability, capacity planning, upgrades, security hardening, compliance support, and operational documentation. The role involves restricted and disconnected environments, automated security tooling, collaboration with government and engineering stakeholders, technical leadership, and mentoring.

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.

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.