Navy Federal Credit Union offers a meaningful career experience with an energized, engaged culture and generous pay and benefits. This onsite Principal AI Engineer role resides in the AI Center for Enablement in Vienna, VA, focusing on end-to-end delivery of production‑grade AI capabilities that leverage large language models and agentic workflows.
Join a team that values accountability, collaboration, and technical excellence as you drive transformative AI initiatives across the organization.
Responsibilities
- Lead end‑to‑end AI solution delivery from discovery through production, ensuring predictable execution, risk oversight, and measurable business value.
- Develop and communicate technical delivery plans with milestones, dependencies, and release readiness criteria, surfacing tradeoffs and guiding decisions.
- Translate business problems into backlog‑ready designs, keeping engineering, security, architecture, and business stakeholders aligned.
- Design and deploy scalable AI systems, including data pipelines, model training and inference patterns, and integration with applications and services.
- Build and operate AI solutions using modern platforms and orchestration patterns such as LLM agents, RAG, grounding, evaluation gates, and safety controls as appropriate.
- Institute and enforce engineering discipline covering code quality, automated testing, performance tuning, observability, and runbooks for AI services.
- Lead by example in debugging complex issues, delivering critical path components, and unblocking teams through hands‑on contributions.
- Create executive‑level narratives (1‑pagers, readouts, decks) that clearly articulate problem statements, approaches, progress, risks, decisions, and business impact.
- Present technical strategies and delivery status to senior leadership and diverse audiences, balancing depth and accuracy.
- Own the technical storytelling for major milestones such as architecture approvals, governance checkpoints, and 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 and best practices and participate in model lifecycle governance, including evaluation and transparency measures.
- Contribute to technology roadmaps and guidance that standardize delivery and improve operational resilience.
- Mentor engineers, lead design and code reviews, foster communities of practice, and systematically raise AI engineering capability across delivery teams.
- Demonstrate end‑to‑end accountability for strategy, architecture, standards, roadmap, adoption, and operational maturity of a defined technical domain, serving as a trusted subject‑matter authority.
- Complete work with minimal supervision.
Requirements
- Bachelor’s degree in Computer Science, Statistics, Engineering or a related field, or an equivalent combination of education, training and experience.
- 7–10 years of experience in AI or a related field.
- Experience with modern Generative AI and agentic patterns including LLM workflows, orchestration frameworks, RAG, grounding, evaluation, and safety controls.
- Proven ability to establish AI standards, best practices, and scalable enablement mechanisms with reference architectures and reusable components.
- Track record of driving GenAI Code Assistants adoption (for example GitHub Copilot) to boost developer productivity with measurable value.
- Hands‑on experience building production grade AI agents using leading platforms such as Azure AI Foundry, and related technologies like MCP or A2A.
- Experience with data platforms (e.g., Databricks) and organizing, cataloging, and chunking of unstructured data for scalable Generative AI solutions and knowledge management.
- Experience with vector stores and graph databases to manage complex relationships for AI applications such as recommendations.
- Robust background in Azure AI and data solutions with API integrations for accessing LLMs.
- 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 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 automated testing.
- Experience with Agile or SAFe delivery and CI/CD practices for AI solutions.
- Significant experience working with structured and unstructured data and developing sophisticated algorithms to automate processes and tasks.
- Advanced knowledge of current AI technologies and concepts.
Technologies: Azure AI Foundry, MCP, A2A, Databricks, GitHub Copilot, Python, Azure, vector stores, graph databases
Location and Hours
Location: 820 Follin Lane, Vienna, VA 22180 (onsite)
Hours: Monday to Friday, 8:00 AM to 4:30 PM
About Navy Federal
Navy Federal provides more than a job; it offers a meaningful career experience with a culture that is energized, engaged and committed, and a strong appreciation for teams through competitive pay and generous benefits.
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