Senior Platform Engineer

Salary: $148,750 - 201,250 per year Requirements:

• Active TS/SCI clearance.
• Hands-on experience building applications or systems with large language models and generative AI.
• Experience designing and implementing AI agents or agentic workflows.
• Strong programming skills in Python and/or another modern programming language.
• Experience integrating AI systems with APIs, enterprise applications, databases, and external tools.
• Experience with at least one agent framework or orchestration approach.
• 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 with platform engineers, government personnel, and multiple technology vendors.
• Preferred: experience with MCP and tool-based agent architectures, multi-agent systems, or agent orchestration.
• Preferred: experience with OpenAI, Anthropic, Google, NVIDIA, or comparable foundation model ecosystems, and with NVIDIA NIM, NeMo, or similar model-serving technologies.
• Preferred: experience with Red Hat OpenShift AI, Kubernetes-based AI platforms, agent evaluation, observability, guardrails, policy enforcement, human-in-the-loop workflows, or AI safety controls.
• Preferred: experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks; RAG pipelines, vector databases, knowledge graphs, or enterprise search.
• Preferred: experience deploying AI in Secret or TS/SCI environments, disconnected or air-gapped environments, or developer and DevSecOps workflows.
• Preferred: familiarity with GitLab, Jira, Confluence, Xacta, UDS, Zarf, Pepr, Iron Bank, or similar tools and technologies.
• Preferred: experience with GPUs, model serving, inference optimization, AI infrastructure, or taking AI prototypes into repeatable, production-ready capabilities.
• Preferred: ability to assess emerging AI technologies quickly and make pragmatic build, buy, or integration decisions.

• Design, build, and integrate AI agents and multi-agent workflows using modern LLM and agent technologies.
• Develop agents that work with enterprise applications, APIs, data sources, developer tools, and mission systems.
• Connect models, agents, tools, and enterprise data in secure workflows for controlled and classified environments.
• Develop secure ways for agents to discover, access, reason over, and act on approved enterprise data and services.
• Prototype and evaluate emerging models, agent frameworks, and AI capabilities for mission and engineering value.
• Create evaluation and testing frameworks covering agent accuracy, reliability, safety, latency, cost, and task completion.
• Implement guardrails and policy controls for agent behavior, tool and data access, and human approval.
• Integrate agent capabilities with CI/CD and DevSecOps workflows for repeatable development, testing, deployment, and lifecycle management.
• Partner with platform engineers to run AI workloads in Kubernetes and OpenShift environments.
• Collaborate with commercial AI and technology partners to connect their capabilities to the wider engineering ecosystem.
• Troubleshoot issues across models, agent frameworks, APIs, data sources, networking, identity, and infrastructure.
• Establish 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 developed into reusable capabilities.
• Track advances in agentic AI and assess their suitability for secure government environments.

• Agentic AI
• AI
• AI Agents
• CI/CD
• Cloud
• Confluence
• DevSecOps
• Git
• GitLab
• GitOps
• Support
• JIRA
• Kubernetes
• LLM
• MCP
• Model Serving
• OpenShift
• Python
• RAG
• Security
• AWS
• Azure
• GCP
• Helm
• Kustomize
• Pulumi
• Terraform

More:

We are Defense Unicorns, a mission-focused company that streamlines secure software delivery so our customers can focus on critical challenges. Our team includes innovators, software engineers, and veterans with extensive federal technology experience. We build secure continuous integration and delivery solutions, combining security pipelines, testing, and deployment automation, and support defense and civil agencies as well as public and private sector mission leaders. Our work spans Kubernetes, cloud environments, infrastructure as code, continuous delivery, GitOps, containers, and open-source cloud-native technologies. This senior AI engineering role embeds with a government team and its technology partners to bring AI capabilities from prototypes into secure, reliable production workflows; it is focused on practical engineering rather than research alone. The position is remote within the United States. The compensation range is $148,750–$201,250 USD, with final packages based on experience and national benchmarking. Our benefits include medical, dental, and vision coverage with premiums fully paid by the company, a health savings account, life insurance, and disability insurance.

last updated 42 week of 2026