Senior AI Engineer (FedD239)

Responsibilities

Design, build, and integrate AI agents and multi-agent workflows within secure enterprise and government environments. Collaborate with platform engineers to operationalize AI workloads and ensure reliable, mission-relevant performance.

Requirements

Requires an active Top Secret clearance and hands-on experience building applications with large language models and agentic frameworks. Candidates must possess strong programming skills in Python and experience integrating AI systems with enterprise data and infrastructure.

Skills

• Python
• Large language models
• AI agents
• Kubernetes
• OpenShift
• RAG
• Vector databases
• CI/CD
• DevSecOps
• Prompt engineering
• System design
• API integration
• Git
• Cloud computing
• Infrastructure as code
• Model orchestration

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Health savings account
• Life insurance
• Disability insurance
• 401k retirement plan
• Company stock options
• Home office budget
• Flexible time off
• Paid parental leave
• Training reimbursement
• Conference reimbursement

About this role

Senior Platform Engineer
AI Engineering | Forward Deployed Engineering
Role Description
Defense Unicorns is seeking a senior AI Agentic Engineer to embed with a government team and its technology partners building an emerging AI-powered engineering ecosystem.
This role will focus on designing, integrating, and operationalizing AI agents and agentic workflows within a secure enterprise environment. The engineer will work across large language models, agent frameworks, enterprise data, APIs, developer tooling, and platform services to turn AI capabilities into reliable, mission-relevant workflows.
The ideal candidate is a hands-on engineer who understands both the capabilities and limitations of modern AI systems and can rapidly translate emerging technologies into working solutions. They should be comfortable experimenting with new models and agent frameworks, integrating agents with enterprise systems and tools, evaluating performance and reliability, and working alongside platform engineers and commercial technology partners.
This is not a research-only role. The focus is on moving from AI concepts and prototypes to secure, useful, repeatable agentic capabilities that can operate in production environments.
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 that can be deployed 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 where they can create 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.
Minimum Experience and Qualifications
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.
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 remaining isolated demos.
Agents reliably connect to approved enterprise data, tools, and services, with appropriat...

About Defense Unicorns

Software Development · 51-200 employees · headquartered in Colorado Springs, Colorado · founded 2021