Senior AI Engineer (FedD239) New Springfield, VA

Defense Unicorns; Springfield (VA); Hybrid; USD 180,000 - 280,000; Full time

Job summary

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. You will design, integrate, and operationalize AI agents and agentic workflows within secure enterprise environments using LLMs, agent frameworks, and enterprise data.

The role emphasizes hands-on implementation, rapid prototyping, and collaboration with platform engineers and partners to move concepts to secure, repeatable

Qualifications

• Hands-on experience building applications or systems with large language models.
• Experience designing and implementing AI agents or agentic workflows.
• Strong programming in Python or similar language.
• Experience integrating AI systems with APIs, enterprise apps, databases, external tools.
• Experience with agent frameworks or orchestration approaches.
• Knowledge of RAG, embeddings, vector databases, tool calling, and prompt engineering.
• Experience with CI/CD in Git-based environments.
• Ability to prototype, test, and iterate in ambiguous environments.
• Understanding of software engineering fundamentals and system design.
• Ability to collaborate with platform engineers, government personnel, and vendors.

Responsibilities

• Design, build, and integrate AI agents and multi-agent workflows with LLMs.
• Develop agent capabilities interacting with enterprise apps, data sources, and tools.
• Create secure, production-ready agentic workflows for enterprise data and services.
• Integrate models, agents, tools, and data into governed workflows.
• Prototype and evaluate models and agent frameworks for mission value.
• Develop testing frameworks for agent accuracy, reliability, and latency.
• Implement guardrails and policy controls for agent behavior and access.
• Integrate agentic workloads with CI/CD and DevSecOps practices.
• Collaborate with platform engineers to deploy on Kubernetes/OpenShift.
• Work with commercial partners to integrate capabilities into the ecosystem.
• Troubleshoot across models, APIs, data sources, networking, identity, and infra.
• Document architectures, patterns, and operational procedures.
• Identify reusable capabilities to productize in secure gov environments.

Skills

LLMs

Agent frameworks

Python

APIs

CI/CD

Kubernetes

OpenShift

RAG

Tools

NVIDIA NeMo

Job description

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

• 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