Principal AI Engineer (Agentic AI)

Overview

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 reviews, develop communities of practice, and systematically raise AI engineering capability across delivery teams.
• Demonstrates end-to-end accountability for the strategy, architecture, standards, roadmap, adoption, and operational maturity of an assigned technical domain. Serves as the organization's recognized subject-matter authority while ensuring the domain's capabilities are reusable, measurable, governed, and effectively adopted across delivery teams.
• Complete work with minimal supervision.

Qualifications

• Bachelor's Degree in Computer Science, Statistics, Engineering or related field, or the equivalent combination of education, training and experience.
• 7-10 years of experience in AI or similar.
• Experience with modern Generative AI and agentic patterns (e.g., LLM workflows, orchestration frameworks, RAG, grounding, evaluation, safety controls).
• Experience establishing AI standards, best practices, and scalable enablement mechanisms (reference architectures, reusable components).
• Proven track record of driving and coordinating use of GenAI Code Assistants (GitHub Copilot, etc.) to drive Developer Productivity initiatives across the organization with clear value metrics.
• Hands-on experience building production grade AI agents using industry leading platforms (Azure AI Foundry, etc.) and related technologies such as MCP or A2A.
• Experience with data platforms (Databricks, etc.) and organizing, cataloging and chunking of unstructured data for scalable Generative-AI solutions and robust knowledge management.
• Experience with vector stores and graph databases for managing complex relationships to use in AI applications such as recommendation systems.
• Robust experience in Azure AI/Data solutions and a deep understanding of the evolving AI landscape, with proven track record in API integrations for accessing LLMs.
• Demonstrated experience conducting threat modeling and implementing security controls for AI systems, including prompt-injection defenses, data-loss prevention, agent authorization, secure tool execution, secrets management, and adversarial testing.
• Experience operationalizing Responsible AI and model-risk requirements through technical controls, evaluation criteria, documentation, human-in-the-loop patterns, traceability, and auditable evidence.
• Advanced software engineering skills in Python and API/service development, with experience designing distributed, resilient, containerized systems and implementing automated testing
• Experience with Agile/SAFe delivery and CI/CD practices for AI solutions.
• Significant experience working with structured and unstructured data.
• Significant experience in developing sophisticated algorithms to automate processes and tasks.
• Advanced knowledge of current AI technologies and concepts.

Additional Information

Hours:

• Monday - Friday, 8:00AM - 4:30PM

Location:

• 820 Follin Lane, Vienna, VA 22180

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