Senior AI Engineer (FedD239)

defenseunicorns; Springfield (VA); On-site; USD 140,000 - 210,000; Full time

Job summary

Defense Unicorns seeks a Senior Platform Engineer to embed with a government team and technology partners, building an AI-powered engineering ecosystem. The role focuses on designing, integrating, and operationalizing AI agents and workflows across LLMs, agent frameworks, enterprise data, APIs, tools, and platform services to deliver reliable, mission-relevant capabilities in production.

This hands-on position requires rapid prototyping, evaluation, and collaboration with platform engineers,

Qualifications

• Active Top Secret clearance is required.
• Hands-on experience building apps with LLMs and generative AI.
• Experience designing and implementing AI agents or agentic workflows.
• Strong Python programming skills.
• Experience integrating AI systems with APIs, enterprise apps, databases and tools.
• Experience with one or more agent frameworks or orchestration approaches.
• Knowledge of RAG, embeddings, vector databases, tool calls, structured outputs, and prompt engineering.
• Experience with Git-based, automated CI/CD environments.
• Ability to prototype, test, and iterate in ambiguous environments.
• Solid fundamentals in software engineering, including APIs, debugging, and system design.
• Ability to collaborate with platform engineers, government personnel, and multiple vendors.

Responsibilities

• Design, build, and integrate AI agents and multi-agent workflows.
• 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 for deployed environments.
• Work with LLMs, RAG, embeddings, vector databases, tool/function calling, structured outputs, MCP, and related technologies.
• Develop mechanisms for agents to securely access approved enterprise data and services.
• Prototype and evaluate emerging models and agent frameworks for measurable value.
• Develop evaluation frameworks for agent accuracy, reliability, latency, and cost.
• Implement guardrails and policy controls governing agent behavior and data access.
• Integrate agentic capabilities with CI/CD and DevSecOps workflows.
• Collaborate with platform engineers to operationalize AI workloads within Kubernetes/OpenShift environments.
• Work with commercial AI partners to integrate capabilities into broader engineering ecosystem.
• Troubleshoot issues across models, agent frameworks, APIs, data sources, networking, and infrastructure.
• Develop reusable patterns for agent deployment, observability, and lifecycle management.
• Document architectures and patterns for agent implementations.

Skills

Top Secret clearance

LLM / Generative AI

AI agentic workflows

Python

API integration

Agent frameworks

RAG / embeddings

Git-based CI/CD

Prototype / debugging

Software fundamentals

collaboration with government vendors

Tools

Kubernetes/OpenShift

Git / CI/CD tooling

LangChain / LangGraph

NVIDIA NIM / NeMo

Job description

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 compa