Senior Platform Engineer (FedD239)

Defense Unicorns•Springfield, VA

30d

About The Position

Defense Unicorns is seeking a senior AI agentic engineer to embed with a government team and its technology partners building an AI-powered engineering ecosystem. The role focuses on designing, integrating, and operationalizing AI agents and agentic workflows in a secure enterprise environment. The engineer will work across large language models, agent frameworks, enterprise data, APIs, developer tooling, and platform services to develop reliable, mission-relevant workflows. This is a hands-on engineering role focused on moving AI concepts and prototypes into secure, useful, repeatable capabilities that can operate in production—not research alone. The engineer will experiment with models and agent frameworks, integrate agents with enterprise systems and tools, evaluate performance and reliability, and collaborate with platform engineers and commercial technology partners. Success means moving agentic capabilities quickly from prototype to real government workflows; ensuring agents connect reliably and securely to approved data, tools, and services; measuring accuracy, reliability, security, and operational performance; creating reusable agent patterns; and integrating AI across the broader platform architecture. The engineer will also serve as a trusted AI technical advisor to the Mission Hero, identifying opportunities for agentic technology and recognizing when AI is not the right solution.

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 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.

Nice To Haves

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.

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.
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 evaluate emerging models, agent frameworks, and AI capabilities for 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 for agent behavior, tool and 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 in 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 evolving agentic AI technologies and assess their applicability to secure government environments.