Defense Unicorns is seeking a Senior AI Agentic Engineer to embed with a government team in Springfield, VA (onsite). The role is focused on designing, integrating, and operationalizing AI agents and agentic workflows in secure enterprise environments, with emphasis on moving prototypes into production-ready, mission-relevant capabilities.

Responsibilities

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 identify measurable 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 to support repeatable development, testing, deployment, and lifecycle management.
Collaborate with platform engineers to operationalize AI workloads within Kubernetes/OpenShift environments.
Work with commercial AI and technology partners to integrate their capabilities into the broader engineering ecosystem.
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 into reusable capabilities.
Stay current with rapidly evolving agentic AI technologies and assess their applicability to secure government environments.

Requirements

Active Top Secret clearance.
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.

Technologies

Python, LLMs, AI agents, multi-agent workflows
RAG, embeddings, vector databases, tool/function calling, structured outputs, MCP
Kubernetes, OpenShift, CI/CD, DevSecOps, Git-based CI/CD environment
Top Secret clearance
OpenAI, Anthropic, Google, NVIDIA, NIM, NeMo, Red Hat OpenShift AI
LangGraph, LangChain, LlamaIndex, Semantic Kernel, knowledge graphs, enterprise search
Secret, TS/SCI, air-gapped
GitLab, Jira, Confluence, Xacta, NVIDIA NIM, UDS, Zarf, Pepr, Iron Bank
Kubernetes-based AI platforms, GPUs, inference optimization, AI infrastructure

Benefits

Medical/Dental/Vision
Premiums are 100% Company Paid
Health Savings Account
Life Insurance
Disability Insurance
401k Retirement Plan
Company Stock Options
Home Office Budget
Flexible Time Off (FTO) for full-time employees, plus all Federal Holidays, one week for Thanksgiving, and two weeks for Christmas and New Year’s
Paid Parental Leave
Reimbursement for approved trainings/subscriptions
Conferences (travel, lodging, and fees)

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 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.

What Success Looks Like

Agentic capabilities move from prototype to mission use quickly, with working agents integrated into real government workflows.
Agents reliably connect to approved enterprise data, tools, and services, with appropriate identity, permissions, guardrails, and human-in-the-loop controls.
AI capabilities are measurable and trustworthy, with repeatable evaluation methods for accuracy, reliability, task completion, security, and operational performance.
Reusable agent patterns emerge, reducing engineering effort required to build and deploy the next agent or AI-enabled workflow.
AI integrates cleanly with the broader platform architecture across data, DevSecOps, Kubernetes/OpenShift, model, and application layers.
The engineer acts as a trusted AI technical advisor, identifying where agentic technology can create mission value and where it is not the right solution.

What We Work On

Kubernetes
Cloud environments (AWS/GCP and Azure)
Infrastructure-as-code (such as Terraform/Pulumi)
Continuous delivery and automation tooling
GitOps
Containers
CNCF projects and open source products and packages
Helm/Kustomize-Value Stream Mapping
Building and improving security delivery
Building and improving Kubernetes and cloud-native applications

Who We Are

Defense Unicorns delivers mission value by streamlining software delivery so customers can focus on key challenges. The team includes innovators, software engineers, and veterans with decades of experience delivering technology programs across the federal market.

What We Do

Create and deliver secure solutions for continuous software integration and delivery.
Consolidate best practices for security pipelines, testing, and deployment automation to meet high security requirements.
Build solutions that are agnostic by design, supporting a robust ecosystem of secure, cloud-native software solutions for enterprise customers inside and outside the federal market.

Who We Serve

Defense Unicorns serves mission-focused leaders across public and private enterprises, supporting defense and civil agencies across the U.S. government and working with creators of leading-edge software solutions to improve security and consumability of commercial software products.

Compensation: USD 148,750 - 201,250 per year. Location: Springfield, VA (onsite).

Senior AI Engineer

Job Description