SME AI Governance Specialist
$131,300–$237,350 year
Remote · United States
Job Summary
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, architect, engineer, 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. Light travel may be required.
Required Qualifications
- Active Secret clearance with TS/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
- 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 DevSecOps 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 DevSecOps 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 NLP tasks such as entity extraction, summarization, and semantic search
- Working knowledge of LLMs and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
- Experience using vector databases (e.g., Pinecone, Weaviate, FAISS)
- Familiarity with deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)
- Clearance Required: Active Secret clearance at time of consideration
- SAFe Agilist (preferred)
- Experience operating within SAFe or large-scale Agile frameworks
- Experience with DoD systems and environments
- Familiarity with NIST security controls and Zero Trust compliance
- Experience with AI/ML model deployment and management
- Strong communication and collaboration skills
- Experience with open-source tools and services integration
- Experience supporting AI governance in multi-enclave DoD environments
- Experience implementing model evaluation, bias mitigation, and drift detection frameworks
- Experience integrating AI governance with enterprise data governance programs
- Experience supporting enterprise-scale AI/ML platforms and model registries
- Familiarity with emerging AI policy frameworks, model risk management, and AI safety standards
- Experience designing and implementing safety, guardrails, and bias-mitigation strategies for autonomous agents and multiagent systems
- Experience integrating agents with cloud-native workflows, streaming data pipelines, and real-time decision-making environments
- Familiarity with evaluation and observability tools for AI agents, such as LangSmith, OpenAI Evals, or custom telemetry systems
- Experience with AI service integration such as NIMS, Azure OpenAI, Bedrock, GCP Vertex AI
- Hands-on GPU programming experience for ML workloads using CUDA, PyTorch, or TensorFlow
- Experience with DoD security controls and Zero Trust
- Active TS/SCI clearance (preferred)
- Experience operating within SAFe or large-scale Agile frameworks
- Experience with DoD systems and environments
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