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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Platform Engineer - **Company:** Defense, Llc - **Location:** United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** LangGraph Framework, Xacta, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Layers, Confluence, JIRA, Microsoft Azure, Computer Programming, Databases, Continuous Delivery, Continuous Integration, Data Security, Software Debugging, Programming Tools, Graph Database, Python (Programming Language), Open Source Technology, OpenShift, Commercial Software, Inference Optimization, OpenAI, Software Safety, Software Engineering, Systems Integration, AI Infrastructure, Enterprise Search, Enterprise Data Management, Pulumi, Graphics Processing Unit (GPU), Google Cloud, Enterprise Software Applications, Cloud Platform System, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Application Programming, Llamaindex, Generative AI, Agentic-AI, Gitlab, Git, AI Platforms, Kubernetes, Low Latency, Machine Learning Operations, Nim (Programming Language), Invoking Functions, Artificial Intelligence Governance, Model Context Protocol, Model Inference, Terraform, Semantic Kernel, Software Version Control, Devsecops, Human in the Loop, Programming Languages - **Published:** October 2, 2026 - **Apply:** https://www.dice.com/job-detail/1ac2c9e4-e916-401c-9ad2-6fa3b790d97f ## About the Role * Active TS/SCI 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 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. ## Description 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., * Agentic capabilities move from prototype to mission use quickly, with working agents integrated into real government workflows rather than remaining isolated demos. * Agents reliably connect to approved enterprise data, tools, and services, with appropriate identity, permissions, guardrails, and human-in-the-loop controls. * AI capabilities are measurable and trustworthy, with repeatable evaluation methods for accuracy, reliability, task completion, security, and operational performance. * Reusable agent patterns emerge, reducing the engineering required to build and deploy the next agent or AI-enabled workflow. * AI integrates cleanly with the broader platform architecture, working effectively across the data, DevSecOps, Kubernetes/OpenShift, model, and application layers rather than creating another technology silo. * The engineer becomes a trusted AI technical advisor to the Mission Hero, proactively identifying where agentic technology can create mission value-and equally important, where AI is not the right solution., Defense Unicorns' customers are mission-focused leaders across public and private enterprises. We proudly support defense and civil agencies across the U.S. government and we work closely with the creators of leading-edge software solutions to deliver value to the mission-owner by improving the security and consumability of commercial software products. What We Work On * Kubernetes * Cloud Environments (AWS/Google Cloud Platform and Azure) * Infrastructure-as-code (like Terraform/Pulumi) * Continuous Delivery and automation tooling * GitOps * Containers * CNCF projects and open source products and packages * Helm/Kustomize-Value Stream Mapping * Building and improving security delivery * Building Kubernetes and cloud native applications ## Related Videos - [Building a Multi-Agent Orchestration Engine That Actually Follows the Rules](https://www.wearedevelopers.com/videos/100159-building-a-multi-agent-orchestration-engine-that-actually-follows-the-rules) - [GitOps for the people](https://www.wearedevelopers.com/videos/461-gitops-for-the-people) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [ I Gave a Video Editor More Autonomy Than a Trading Bot. On Purpose.](https://www.wearedevelopers.com/magazine/773-i-gave-a-video-editor-more-autonomy-than-a-trading-bot-on-purpose) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)