MLOps)

Navy Federal Credit Union•Vienna, VA

16h•Onsite

About The Position

The Director, Enterprise AI Operations (LLMOps / AgentOps / MLOps) leads the enterprise capabilities, platforms, standards, and operating model required to deploy, govern, operate, and continuously optimize AI solutions at scale. This leader is responsible for establishing the production frameworks, operational services, engineering practices, and automated governance controls that enable AI delivery teams to move solutions efficiently from experimentation into secure, reliable, and well-governed production environments. Working closely with Infrastructure, Data, Cybersecurity, Architecture, Risk, and Business teams, the Director will drive adoption of enterprise AI capabilities that accelerate delivery while maintaining operational excellence, regulatory compliance, and responsible AI practices. The role will build and lead a multidisciplinary team, manage strategic vendor relationships, and influence enterprise platform and operating-model decisions that enable trusted AI solutions for members and team members.

Requirements

Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline, or the equivalent level of work experience
10+ years of experience in software engineering, platform engineering, AI/ML operations, DevOps, Site Reliability Engineering, or related disciplines.
6+ years of leadership experience managing/leading engineering, operations, platform, or AI-focused teams.
Experience operationalizing machine learning, generative AI, or agentic AI solutions in large-scale enterprise environments.
Strong understanding of AI lifecycle management, including deployment, monitoring, governance, evaluation, continuous improvement, and retirement.
Experience establishing operational frameworks, standards, and governance practices for enterprise technology platforms.
Knowledge of AI observability, reliability engineering, incident management, and operational excellence practices.
Understanding of AI security, privacy, identity and access management, compliance, model risk, and responsible AI principles.
Experience managing large initiatives, vendor relationships, technology investments, budgets, and strategic roadmaps.
Proven ability to influence senior stakeholders and drive alignment across technology and business organizations.
Strong communication skills with the ability to translate complex technical concepts into business outcomes and executive recommendations.

Nice To Haves

Experience with Microsoft AI technologies, Azure AI Foundry, Copilot Studio, Databricks, MLflow, LangChain, LangGraph, LangSmith, or similar platforms.
Experience with generative AI, RAG architectures, vector search, orchestration frameworks, and agentic AI solutions.
Experience supporting highly available, member-facing, or mission-critical AI applications.
Familiarity with cloud-native architectures, automation, CI/CD, infrastructure-as-code, and modern engineering practices.
Experience implementing AI governance, model risk management, AI evaluation, FinOps, or responsible AI controls.
Experience within financial services, credit unions, or other highly regulated industries.
Advanced degree and/or relevant cloud, AI, or machine learning certifications.

Responsibilities

Lead enterprise LLMOps, AgentOps, and MLOps capabilities across the AI lifecycle, including deployment, evaluation, monitoring, operational governance, and continuous improvement.
Establish enterprise operational standards, controls, and operating models for managing AI solutions in production.
Own the roadmap, service model, engineering standards, and adoption strategy for shared AI operational capabilities.
Define and implement AI observability, reliability, production-readiness, and operational support frameworks to ensure service quality and resilience.
Partner with enterprise control functions to operationalize AI evaluation, governance, and risk-management requirements that promote quality, safety, compliance, and responsible AI outcomes.
Lead AI FinOps capabilities, including cost attribution, consumption monitoring, optimization, and capacity planning.
Partner with Security, Risk, Audit, Compliance, Architecture, and Infrastructure teams to translate enterprise policies and regulatory requirements into effective operational controls.
Develop executive reporting and performance metrics related to AI adoption, reliability, utilization, operational risk, cost efficiency, and production health, while enabling business owners to measure value realization.
Evaluate emerging technologies and industry trends to improve enterprise AI operational maturity and scalability.
Build and lead a high-performing multidisciplinary team while managing strategic technology partners and driving adoption across AI engineering and delivery organizations.