Bachelor's degree in Computer Science, Data Science, or related field with 10+ years of experience; or 14+ years of experience without a degree
Proven experience in mission environments
Familiarity with frameworks like LangGraph, Agno, or NVIDIA NeMo Agent Toolkit
Experience with model inference/serving platforms such as NVIDIA NIM, vLLM, Ollama, LiteLLM
Knowledge of MCP (Model Context Protocol) or similar integration standards
Experience with AI evaluation frameworks and model benchmarking
Active TS/SCI clearance with polygraph
Responsibilities
Provide embedded operations support and rapid prototyping for AI/ML tools
Design and integrate LLM/agent orchestration workflows using agentic frameworks
Tune model gateway routing logic for optimal performance and mission fit
Develop RAG pipelines and memory services for agent access to mission knowledge
Implement MCP-based tool-broker integrations for secure data access
Evaluate and tune models and agent behaviors, building automated evaluation pipelines
Research and evaluate emerging AI tools to keep capabilities current
Benefits
Onsite work environment at Fort Meade, MD
Opportunity to work on cutting-edge AI innovations
Engagement in a mission-critical role with significant impact
Collaboration with data scientists and mission analysts
Access to advanced AI frameworks and tools
Full Job Description
Requisition #: 1921
Job Title: SME AI/ML Engineer
Location: Fort Meade, MD | Onsite
Clearance Level: TS/SCI w/ Polygraph, Must Have Clearance to Start
Job Description
Ready for a challenge that puts you at the center of AI innovation for cyber operations? Are you an experienced AI/ML engineer who thrives in a fast-paced environment, enjoys turning research and prototypes into real mission capability, and wants to help shape how AI is built and delivered across a national mission?
Our team is standing up an AI Enterprise capability - the central hub that mission teams turn to when they want to move an AI solution from prototype to a secure, governed, enterprise-scale service. As a Senior AI/ML Engineer on this team, you will design and integrate the models, agents, and AI workflows that run on that platform, with significant, visible impact on the mission.
What You'll Be Doing:
Provide embedded operations support, capability integration, and rapid prototyping for AI/ML-enabled tools in support of our customer. Design and integrate LLM/agent orchestration workflows using agentic frameworks such as LangGraph, Agno, and the NVIDIA NeMo Agent Toolkit.
Tune model gateway routing logic to select, evaluate, and optimize across multiple frontier and open-weight models for cost, performance, and mission fit.
Develop and integrate RAG pipelines, vector databases, and memory services to give agents access to mission knowledge and context.
Implement MCP (Model Context Protocol)-based tool-broker integrations so agents can safely and securely access enterprise tools and data.
Evaluate, benchmark, and tune models and agent behaviors; build automated evaluation pipelines to track quality, safety, and performance over time.
Partner with data scientists and mission analysts to translate research and prototypes into production-grade AI capabilities for the command.
Implement AI governance guardrails - content/safety filters, policy enforcement, and approval gates - for agentic workflows, and document model/agent architectures and evaluation results for reuse.
Research and evaluate emerging LLM, agent, and AI tooling to keep the command's AI capability current and adaptable.
Basic Qualifications
Bachelor's degree from an accredited college or university in Computer Science, Data Science, or a related discipline and 10+ years of experience; 4 additional years of experience may be considered in lieu of degree
Experience supporting mission environments
Experience with frameworks such as LangGraph, Agno, or the NVIDIA NeMo Agent Toolkit
Experience with model inference/serving platforms (NVIDIA NIM, vLLM, Ollama, LiteLLM)
Experience with MCP (Model Context Protocol) or similar tool-broker/integration standards
Experience with AI evaluation frameworks, model benchmarking, and observability (logs/metrics/traces) for AI workloads
