Senior AI Engineer

Defense Unicorns; Springfield (VA); On-site; USD 149,000 - 201,000; Full time

Benefits offered by this job

Medical/Dental/Vision

Company Paid premiums

Home Office Budget

Stock Options

Paid Parental Leave

Conferences (travel, lodging, fees)

FTO + Federal Holidays

Job summary

Defense Unicorns seeks a Senior AI Agentic Engineer to embed with a government team and deliver AI agent and multi-agent capabilities in secure enterprise environments.

Role focuses on productionizing agent prototypes, evaluating guardrails, and integrating with Kubernetes/OpenShift and CI/CD workflows for mission-critical workflows.

Qualifications

• Active Top Secret clearance.
• Hands-on with LLMs and generative AI.
• Experience designing AI agents or agentic workflows.
• Strong Python or modern language programming.
• API/integration experience with enterprise data.

Responsibilities

• Design, build, and integrate AI agents and multi-agent workflows.
• Develop agent capabilities for enterprise apps, data, and tools.
• Securely integrate models and data into controlled workflows.
• Apply RAG, embeddings, vector databases, and tool calling.
• Prototype, evaluate, and test agent frameworks and AI capabilities.
• Implement guardrails, policy controls, and HIL workflows.

Skills

AI agentic engineering

Python

LLMs

Kubernetes

OpenShift

RAG

Vector databases

Git-based CI/CD

Security clearance

Tools

LangChain

LlamaIndex

Semantic Kernel

OpenAI

NVIDIA NIM/NeMo

Job description

Defense Unicorns is seeking a Senior AI Agentic Engineer to embed with a government team and deliver AI agent and multi-agent capabilities in secure enterprise environments. This role emphasizes transitioning agent prototypes into production-ready, mission-relevant workflows, including evaluation, guardrails, and platform integration.

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.
• Apply LLM capabilities and agentic approaches including RAG, embeddings, vector databases, tool/function calling, structured outputs, and MCP.
• Create 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.
• Build evaluation and testing frameworks covering accuracy, reliability, safety, latency, cost, and task completion.
• Implement guardrails and policy controls for 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 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
• All full-time Unicorns Flexible Time Off (FTO) 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 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.

What Success Looks Like

• Agentic capabilities move from prototype to mission use quickly, with working agents integrated into real government workflows rather than isolated demos.
• 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 the engineering 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, avoiding additional technology silos.
• The engineer serves as a trusted AI technical advisor to the Mission Hero by identifying where agentic technology can create mission value, and where AI is not the right solution.

What We Work On

• Kubernetes
• Cloud Environments (AWS/GCP and Azure)
• Infrastructure-as-code (like 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 Kubernetes and cloud native applications

Who We Are

Defense Unicorns delivers mission value by streamlining software delivery so customers can focus on the most important challenges. The team shares a vision of freedom and security for the advancement of progress and innovation, emphasizing speed, user experience, and optionality without compromising security. The team includes innovators, software engineers, and veterans with decades of experience delivering technology programs across the federal market.

What We Do

Defense Unicorns creates and delivers secure solutions for continuous software integration and delivery. The company consolidates best practices for security pipelines, testing, and deployment automation to meet the high security requirements valued by mission owners. Solutions are designed to be agnostic so enterprise customers can buy and integrate software more easily inside and outside the federal market.

Who We Serve

Defense Unicorns supports mission-focused leaders across public and private enterprises, including defense and civil agencies across the U.S. government. The team works closely with the creators of leading-edge software solutions to deliver value to mission owners by improving the security and consumability of commercial software products.

Location and Salary

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