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Salary
$155k – $175k per year
Location
In office (Fort Meade)
Seniority
Senior · 8+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 9, 2026. First seen by Alion on Oct 8, 2026. Agiledefense scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Agile Defense is an American government technology services company that has grown rapidly through acquisition into a mid-tier federal contractor, backed by the private equity firm Enlightenment Capital. It provides digital transformation, cloud and infrastructure services, cyber operations, artificial intelligence and mission support to defence, intelligence and civilian agencies, largely through cleared staff embedded with government programme offices. Headquartered in McLean, Virginia, it competes in the segment between the very large primes and the small business set-aside contractors, where consolidation has been rapid.

Requisition #: 1920

Job Title: Senior 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 8+ years of experience
  • Advanced proficiency in Python for AI/ML application development
  • Demonstrated experience building or integrating LLM/agent-based systems in production
  • Experience with retrieval-augmented generation (RAG), vector databases, and prompt/context engineering
  • Ability to manage competing priorities in fast-paced mission environments and identify/escalate operational risks early
  • Active TS/SCI clearance w/Poly

Preferred Skills

  • 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
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