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Defense Unicorns is seeking a senior AI/Agentic Engineer to embed with a government team and technology partners building an emerging AI-powered engineering ecosystem. This is a hands-on engineering role focused on designing, integrating, and operationalizing AI agents and agentic workflows within secure enterprise environments.
You will work across large language models, agent frameworks, enterprise data, APIs, developer tooling, and platform services to translate AI capabilities into reliable, mission-relevant workflows. This is not a research-only role—the focus is on moving from AI concepts and prototypes to secure, useful, repeatable agentic capabilities that operate in production environments.
Key responsibilities include:
- Design, build, and integrate AI agents and multi-agent workflows using modern LLM and agentic technologies
- Develop agent capabilities that interact with enterprise applications, APIs, data sources, developer tools, and mission systems
- Integrate models, agents, tools, and enterprise data into secure workflows deployable in controlled and classified environments
- Work with LLMs, RAG, embeddings, vector databases, tool/function calling, structured outputs, MCP, and related technologies
- Develop mechanisms for agents to securely discover, access, reason over, and act upon approved enterprise data and services
- Prototype and rapidly evaluate emerging models, agent frameworks, and AI capabilities to determine mission or engineering value
- Develop evaluation and testing frameworks for agent accuracy, reliability, safety, latency, cost, and task completion
- Implement guardrails and policy controls governing agent behavior, tool access, data access, and human approval points
- Integrate agentic capabilities with CI/CD and DevSecOps workflows
- Collaborate with platform engineers to operationalize AI workloads within Kubernetes/OpenShift environments
- Work with commercial AI and technology partners to integrate their capabilities
- Troubleshoot issues across models, agent frameworks, APIs, data sources, networking, identity, and infrastructure
- Develop reusable patterns for agent deployment, configuration, observability, evaluation, and lifecycle management
- Document architectures, integration patterns, agent specifications, evaluation results, and operational procedures
- Identify recurring AI engineering challenges that can be standardized, automated, or productized
- Stay current with rapidly evolving agentic AI technologies and assess applicability to secure government environments
Success looks like: agentic capabilities moving from prototype to mission use quickly with working agents integrated into real government workflows; agents reliably connecting to approved enterprise data, tools, and services with appropriate identity, permissions, guardrails, and human-in-the-loop controls; AI capabilities being measurable and trustworthy with repeatable evaluation methods; reusable agent patterns emerging to reduce engineering effort; AI integrating cleanly with broader platform architecture; and becoming a trusted AI technical advisor identifying where agentic technology creates mission value.
Defense Unicorns delivers mission value by streamlining software delivery for defense and civil agencies. The company creates and delivers secure solutions for continuous software integration and delivery, consolidating best practices for security pipelines, testing, and deployment automation to meet high security requirements.
REQUIREMENTS:
Minimum Experience and Qualifications:
- Active TS/SCI clearance (required)
- Hands-on experience building applications or systems using large language models and generative AI
- Experience designing and implementing AI agents or agentic workflows
- Strong programming experience in Python and/or another modern programming language
- Experience integrating AI systems with APIs, enterprise applications, databases, and external tools
- Experience with one or more agent frameworks or orchestration approaches
- Working knowledge of RAG, embeddings, vector databases, tool/function calling, structured outputs, and prompt engineering
- Experience developing software in a Git-based, automated CI/CD environment
- Ability to rapidly prototype, test, troubleshoot, and iterate in ambiguous environments
- Strong understanding of software engineering fundamentals, including testing, version control, APIs, debugging, and system design
- Ability to work effectively alongside platform engineers, government personnel, and multiple technology vendors
Preferred Experience and Qualifications:
- Experience with MCP (Model Context Protocol) and tool-based agent architectures
- Experience with multi-agent systems and agent orchestration
- Experience with OpenAI, Anthropic, Google, NVIDIA, or comparable foundation model ecosystems
- Experience with NVIDIA NIM, NeMo, or similar AI inference/model-serving technologies
- Experience with Red Hat OpenShift AI or Kubernetes-based AI platforms
- Experience with agent evaluation and observability frameworks
- Experience implementing AI guardrails, policy enforcement, human-in-the-loop workflows, or AI safety controls
- Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks
- Experience with RAG pipelines, vector databases, knowledge graphs, and enterprise search
- Experience deploying AI systems in Secret or TS/SCI environments
- Experience with disconnected or air-gapped AI environments
- Experience integrating AI into developer/DevSecOps workflows
- Familiarity with GitLab, Jira, Confluence, Xacta, or similar enterprise engineering and compliance tools
- Experience working with GPUs, model serving, inference optimization, or AI infrastructure
- Familiarity with UDS, Zarf, Pepr, Iron Bank, or similar secure software delivery technologies
- Experience taking AI prototypes into repeatable, production-ready capabilities
- Ability to evaluate new AI technologies quickly and make pragmatic build/buy/integrate decisions
