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Build and operate AWS environments, Terraform infrastructure, CI/CD pipelines, release processes, and DevSecOps controls for federal AI workloads. Secure Amazon Bedrock services, implement observability and cost controls, automate compliance evidence for FedRAMP, NIST 800-53, and CJIS, and support AWS platform engineering engagements. Administer AI development tooling, document standards and runbooks, and coach engineers. The role requires strong AWS, Terraform, GitHub Actions, generative AI, compliance, and automation expertise.

Build and ship generative AI applications for federal clients, including RAG pipelines, tool-using agents, APIs, and user interfaces. Develop with Python, TypeScript, Claude Code, AWS services, and OpenCORA; apply test-driven development, CI/CD, secure coding, observability, and LLM evaluation. Collaborate on requirements, Jira planning, pull requests, testing, releases, and client communication while maintaining traceability and delivering incremental production improvements.

Own requirements analysis for federal AI applications, translating stakeholder needs into use cases, mockups, prototypes, specifications, acceptance criteria, and Jira backlog items. Facilitate user validation, demos, and feedback sessions; maintain traceability from requirements through pull requests; define expected AI behavior; model processes and data flows; and document compliance, privacy, governance, and responsible-AI considerations. Partner with technical and quality teams while supporting SDLC standards and business development.

Leads end-to-end delivery of generative AI applications for federal clients, serving as accountable architect, technical lead, and primary client contact. Designs AWS-based architectures using Bedrock, RAG, agentic workflows, and client integrations; manages agile delivery, backlogs, testing, infrastructure as code, security compliance, and production releases. Mentors engineers, reviews designs and code, gathers user feedback, improves delivery processes, and supports proposals and demonstrations.

Owns quality strategy and testing for AI applications, including automated end-to-end, user-acceptance, security, performance, resilience, and AI-behavior testing. Builds evaluation harnesses, golden datasets, regression suites, safety checks, and CI/CD quality gates. Tests AWS Bedrock solutions, leads defect triage, supports federal compliance evidence, collaborates with users and engineers, and improves testing standards across the SDLC.