Senior Platform Engineer (FedD239)
- Compensation
- Not disclosed
- Location
- Springfield, VA
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Devops
- Posted
Skills
Nice to have
- 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.
Full 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.