Principal AI Engineer (Agentic AI)

The Cognitive and Generative AI Engineering team is responsible for developing and implementing AI-driven solutions that enhance and scale AI adoption across Navy Federal Credit Union. ***Navy Federal Credit Union currently does not provide sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.*** The Cognitive and Generative AI Engineering team is responsible for developing and implementing AI-driven solutions that enhance and scale AI adoption across Navy Federal Credit Union. The Principal AI Engineer is a senior, hands-on technical leader responsible for end-to-end delivery of production-grade AI capabilities that span architecture, implementation, deployment, and operational readiness while translating complex technical work into clear, executive-ready narratives. This is a pivotal role in the AI Center for Enablement, leading the design and implementation of cutting-edge AI systems. This role leverages and adapts state-of-the-art large language models (LLMs) to solve complex business problems and identifies opportunities to build modular and reusable components. The incumbent will collaborate with ETS (Enterprise Technology Services) and Business partners including Enterprise Architecture, Enterprise AI Strategy, and the AI Working Group to build and drive solutions. They will provide delivery and ongoing support for NFCU's data science, advanced analytics, and augmented intelligence technologies, executing on the strategic vision and ensuring the successful implementation of AI initiatives across the organization. Successful candidates will exhibit excellent problem-solving skills, effective communication and analytical skills, as well as strong leadership qualities with a proven track record of delivering measurable outcomes. ### Responsibilities - Lead delivery of AI solutions from discovery through production, ensuring predictable execution, risk management, and measurable value realization. - Define and socialize technical delivery plans, milestones, dependencies, and release readiness criteria; proactively surface tradeoffs and decision points. - Translate business problems into implementable designs and backlog-ready work, maintaining alignment across engineering, security/risk, architecture, and business stakeholders. - Architect and implement scalable AI systems, including data pipelines, model training/inference patterns, and integration into applications/services. - Build and deploy AI solutions leveraging modern AI platforms and orchestration patterns (e.g., LLM/agent workflows, RAG, model grounding, evaluation gates, safety controls) as applicable to the use case. - Establish and enforce strong engineering discipline: code quality, automated testing, performance tuning, observability, and operational runbooks for AI services. - Lead by example in debugging complex issues, completing critical path implementation work, and unblocking teams through direct technical contribution. - Develop concise, executive-ready narratives (1-pagers, readouts, and decks) that clearly communicate: problem statement, approach, progress, risks, decisions needed, and business impact. - Present technical strategies and delivery status to senior leadership and mixed audiences, adjusting depth while preserving accuracy and decision clarity. - Own technical “storytelling” for major milestones (architecture approvals, governance checkpoints, production readiness) with crisp visuals and artifacts. - Define and promote reusable patterns, components, and best practices for AI engineering to accelerate delivery across teams. - Develop and publish AI standards/best practices and participate in model lifecycle governance (e.g., model registration/curation processes, evaluation and transparency measures). - Contribute to technology roadmaps and guidance that standardizes delivery and improves operational resilience. - Mentor engineers, lead design and code rev…