SME AI Governance Specialist

Responsibilities

• Develop and maintain AI governance frameworks ensuring compliance with ethical, legal, and organizational standards
• Conduct risk assessments to identify potential harms, biases, or compliance gaps in AI models and workflows
• Collaborate with engineering, legal, and mission teams to ensure AI solutions align with governance policies
• Prepare, maintain, and execute a System Engineering Plan (SEP) for managing all systems architecture and system engineering aspects
• Design, prepare, and document systems engineering and cybersecurity artifacts for the System
• Conduct systems engineering activities to specify, build, and maintain system engineering designs
• Support the Government in recommending and conducting enterprise system architecture activities
• Define, document, maintain, and promulgate APIs and technical standards for the System
• Design, engineer, integrate, and continuously improve the underlying infrastructure of the System
• Identify, prepare, track, secure, and integrate government, commercial, and open-source tools and services into the System
• Design, architect, engineer, and continuously improve the user interface (UI) and user experience (UX) components
• Build and maintain services and products to make production-ready AI/ML models accessible for customer use
• Design, architect, engineer, and continuously improve all aspects of cybersecurity elements of the System
• Perform site reliability engineering to build and maintain a reliable, scalable, and efficient System
• Participate in the Engineering Control Board (ECB) process for supporting all major engineering milestones and decisions

• Active Top Secret (TS) clearance with SCI eligibility
• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Information Systems, or related technical discipline and 12–15 years of relevant experience OR Master’s degree in a related field and 10–13 years of relevant experience
• Minimum of 8 years of experience in systems engineering, AI governance, data governance, or a related field
• Strong understanding of ethical, legal, and organizational standards for AI systems
• Experience implementing AI/ML governance or responsible AI frameworks in enterprise environments
• Experience evaluating AI/ML models for performance, bias, explainability, and risk
• Experience integrating governance controls into AI/ML Dev Sec Ops  pipelines
• Experience supporting AI systems in cloud-native environments (AWS, Azure, or GCP)
• Proficiency in systems engineering and cybersecurity practices
• Experience with enterprise system architecture activities
• Ability to define and maintain APIs and technical standards
• Experience with cloud environments, data storage, and Dev Sec Ops  practices
• Demonstrated expertise in AI lifecycle management and policy-to-implementation alignment
• Experience developing Agentic AI solutions, including autonomous planning–execution–reflection loops, multi-agent collaboration and coordination, and tool usage patterns including API integration, retrieval-augmented generation (RAG), and memory/context management
• Solid understanding and hands-on experience with generative AI models including prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search
• Working knowledge of Large Language Models (LLMs) and agent frameworks such as Lang Chain, Lang Graph, CrewAI, A2A, MCP, or Auto Gen
• Experience using vector databases (e.g., Pinecone, Weaviate, FAISS)
• Familiarity with deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)