Own technical architecture and implementation decisions for the team's AI applications, documenting key decisions and tradeoffs. Provide technical leadership to junior developers and interns, including assigning and scoping technical work, reviewing code, and guiding their growth. Establish engineering standards for code review, testing, CI/CD, security, and documentation. Set up and manage hosting, deployment pipelines, and data integrations on Azure and internal platforms. Build, test, deploy, and maintain LLM-powered applications and AI agents. Develop tools to assess, grade, and triage RFIs, solicitations, and related requests. Automate proposal and project artifacts, including requirements traceability matrices, CDRL deliverables, presentations, and related documentation. Build document-processing pipelines using OCR, structured extraction, retrieval, and other AI techniques. Integrate AI applications with enterprise tools, internal systems, APIs, and data sources. Move prototypes into production through testing, monitoring, error handling, and performance improvements, and prepare documentation and handover so solutions can be supported long term. Partner with the AI Lead, end users, and technical teams to assess feasibility, refine requirements, and advise on technical risks and tradeoffs.