Senior AI Engineer-Agentic AI

The AI Engineering function in Enterprise Data and Information Management (EDIM) is responsible for delivering AI solutions to drive productivity across Navy Federal Credit Union. The Sr. AI Engineer collaborates with team members in the AI Center for Enablement leading the design and implementation of cutting-edge AI solutions. The role will deliver AI solutions collaborating with stakeholders in Enterprise Technology Services (ETS) and business partners including Enterprise Architecture, Enterprise AI Strategy, and the AI Working Group.

Successful candidates will demonstrate deep expertise in AI engineering, particularly in agent-based systems, exhibit strong leadership and communication skills, and possess a proven ability to drive innovation in a cross-functional environment. The role leverages Large Language Models (LLMs) and Agentic frameworks to build intelligent agents, and modular components to address complex business challenges. The engineer will collaborate with team members working in data science and advanced analytics to implement AI. The role will provide technical leadership and mentorship to junior engineers and ensure alignment with strategic goals.

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• Collaborate cross-functionally to align agentic AI capabilities with business goals and drive enterprise adoption.
• Define, publish, and socialize technology roadmaps for AI capabilities aligned with business outcomes and enterprise architecture.
• Architect and deliver AI capabilities, foster technological innovation, and improve Member experience.
• Develop agentic workflows using foundational platforms like AI Foundry, ensuring modular and scalable architecture.
• Design and implement intelligent AI agents capable of reasoning, planning, and executing tasks across complex workflows.
• Lead engineering efforts to scale agentic AI platforms, enabling agent orchestration and real-time decisioning.
• Build agentic solutions with orchestration frameworks such as LangChain leveraging A2A (Agent to Agent), MCP (Model Context Protocol), and Memory Management.
• Integrate agentic systems into web frameworks (Django or similar) for real-time decision-making and user interaction.
• Design and implement API integrations for agents to access external tools, services, and data sources.
• Deliver prompt engineering strategies and optimize to drive reliability and accuracy of LLM-based capabilities.
• Ensure compliance and governance through automated ground truth validation, toxicity filtering, and transparency measures.
• Optimize infrastructure for agentic workloads, focusing on compute, latency, and throughput.
• Operationalize agentic AI systems by defining deployment, monitoring, and lifecycle management requirements.
• Lead and support enterprise developer productivity initiatives by leveraging agentic capabilities in AI coding assistants.
• Partner with engineers, data scientists, and product teams to shape strategic goals and direction of the AI Center for Enablement.
• Partner with business stakeholders to evaluate commercial AI solutions versus custom-built options and deliver data-driven Build vs. Buy recommendations.
• Collaborate with technology vendors and professional services firms specializing in AI capabilities to drive value outcomes.
  
   

• 8-10 years of relevant experience in AI, Data Science, ML and related fields.
• Hands-on experience with AI Foundry or similar foundational platforms, particularly in agentic workflow design and deployment.
• Hands-on experience with orchestration frameworks such as LangChain including multi-agent workflows, human-in-the-loop design, and unified observability
• Experience designing and operationalizing Agentic AI platforms, including curating LLMs within a Model Garden, engineering prompts for autonomous task execution, and implementing governance frameworks to ensure safe, scalable deployment
• Proficiency in leveraging advanced techniques including Retrieval Augmented Generation (RAG) to drive model accuracy, minimize hallucinations, and ground the model in facts, specifically when building AI Chatbots
• Experience with document processing services such as Azure Document Intelligence to extract text and structure from documents
• Experience with using vector stores and graph databases in delivering AI solutions
• Experience in API integration enabling agent access to external data sources and web services.
• Experience implementing MCP and A2A capabilities, including asynchronous communication and collaborative task execution.
• Experience with Python, SQL, and web frameworks (Django)