Depending on future opportunities, work may involve building, deploying, and operating AI systems, platforms, infrastructure, and data integrations for commercial and government organizations. Roles may also include engineering delivery, client collaboration, open-source contributions, security, product work, or business functions.
Design and implement a platform for the test and evaluation of AI models and agentic systems. Collaborate with subject matter experts and developers to establish repeatable methodologies for performance benchmarking and failure mode analysis.
The Engineering Architect will own the end-to-end technical design of a secure AI test and evaluation platform for government teams. They will establish architecture standards, resolve complex technical dependencies, and provide strategic guidance on open-source and commercial technology integration.
Build and operate Kubernetes-based infrastructure for AI/ML workloads, including GPU scheduling and multi-tenant isolation. Design and implement platform services for orchestration, data ingest, and model serving while maintaining infrastructure as code.
Collaborate with engineers and clients to define technical objectives, capture requirements, and manage project risks across AI and infrastructure initiatives. Ensure technical deliverables remain aligned with business goals while facilitating clear communication between technical teams and executive stakeholders.
