Role Description

Defense Unicorns is seeking an Applied AI Engineer to embed with a government team and its technology partners building an emerging AI-powered engineering ecosystem.

This role will focus on designing, building, and integrating agentic AI capabilities that allow AI models to interact with tools, access enterprise data, execute multi-step workflows, and support mission applications. The engineer will work across LLM integration, agent orchestration, APIs, retrieval-augmented generation (RAG), and the interfaces between AI applications and existing software platforms.

The ideal candidate is a hands-on software engineer with demonstrated experience building agentic AI applications. They should be comfortable entering an evolving architecture, learning unfamiliar systems quickly, working directly with commercial technology partners, and solving difficult problems involving integration, reliability, performance, and security.

This is not a traditional AI research or model development role. The focus is on building reliable, repeatable agentic capabilities that move beyond experimentation into working software in secure and classified environments.

Responsibilities

• Design, build, and maintain agentic AI applications that integrate LLMs, tools, APIs, and enterprise data to support mission workflows.
• Develop AI agents capable of executing multi-step tasks, interacting with external services, and coordinating actions across existing systems.
• Implement agent orchestration, tool calling, workflow state management, error handling, and appropriate human oversight.
• Build and integrate RAG pipelines, semantic search, embeddings, and vector-based retrieval to provide relevant context for AI applications.
• Develop backend services and APIs using Python and modern software engineering practices.
• Integrate agentic AI capabilities into existing applications, software delivery workflows, and enterprise platforms.
• Establish testing, evaluation, monitoring, and reliability practices for AI agents and LLM-powered applications.
• Collaborate with data engineers to ensure agents can access reliable, relevant, and appropriately governed enterprise data.
• Work with platform engineers to deploy and operate AI applications within Kubernetes/OpenShift environments and integrate them into CI/CD workflows.
• Troubleshoot end-to-end issues involving agent behavior, tool execution, APIs, data access, application services, and infrastructure.
• Work directly with government engineers and commercial technology partners to resolve integration challenges and remove blockers to mission delivery.
• Develop documentation, reusable components, and operational standards that make successful AI implementation patterns repeatable.
• Operate effectively in a fast-moving, ambiguous environment where technologies, security requirements, and mission priorities continue to evolve.

Local to the National Capital Region and able to support onsite work at a government facility in Springfield, VA as mission needs increase.

Clearance & Travel

• Active Top Secret clearance with SCI eligibility required
• Ability to support approximately 2–3 days per week onsite in Springfield, VA, with additional onsite presence as mission needs increase.

Minimum Experience and Qualifications

• Approximately 3–5 years of software engineering experience, or equivalent demonstrated technical capability.
• Hands-on experience building agentic AI applications, including AI agents, LLM tool calling, multi-step workflows, or agent orchestration.
• Strong proficiency with Python and backend software development.
• Experience integrating LLMs into applications using APIs, SDKs, or comparable technologies.
• Experience building and integrating REST APIs and connecting applications with external services or data sources.
• Understanding of agent workflows, tool execution, state management, and error handling.
• Experience with Git, testing, debugging, and modern software development practices.
• Experience working in cloud-native or containerized environments and collaborating with platform engineering teams.
• Ability to troubleshoot application and integration issues across APIs, data services, infrastructure, and AI components.
• Ability to work independently, rapidly learn new technologies, and translate ambiguous mission needs into working software.
• GEOINT experience is preferred, not required.

Relevant agentic AI experience may be demonstrated through professional work, internships, research, open-source contributions, or independent projects.

Preferred Experience and Qualifications

• Experience with agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or comparable technologies.
• Experience developing multi-agent systems, autonomous workflows, or AI-enabled software development tools.
• Experience with Model Context Protocol (MCP), function calling, or other tool integration patterns.
• Experience with RAG, embeddings, vector databases, semantic search, or knowledge graphs.

Mission Focus

Make trusted data available where AI needs it, while establishing the repeatable, secure data patterns required to move from experimentation to operational capability.