Senior Platform Engineer (FedD239)

Description

The company makes secure software delivery easier for federal and commercial customers, working with Kubernetes, cloud platforms, infrastructure as code, GitOps and CI/CD tooling.

You will be a senior AI agentic engineer, placed inside a government group and working alongside its technology vendors to help grow an AI-driven engineering ecosystem.

The work is hands-on: designing and connecting AI agents and agentic workflows in a secure enterprise setting, using LLMs, agent frameworks, enterprise data, APIs, developer tools and platform services.

You should know what modern AI systems can and cannot do, and turn new technologies into working solutions quickly. This is a building role, not a research one. The aim is to take prototypes to secure, repeatable agent capabilities that run in production.

Responsibilities

• Design, build and connect AI agents and multi-agent workflows using current LLM and agent technologies.
• Build agent features that work with enterprise applications, APIs, data stores, developer tools and mission systems.
• Bring models, agents, tools and enterprise data together into secure workflows that can be deployed in controlled and classified environments.
• Work with LLMs, RAG, embeddings, vector databases, tool and function calling, structured outputs and MCP.
• Create ways for agents to find, access, reason over and act on approved enterprise data and services.
• Prototype and test new models and frameworks quickly to find where they add measurable mission or engineering value.
• Build evaluation and test frameworks that measure agent accuracy, reliability, safety, latency, cost and task completion.
• Add guardrails and policy controls over agent behavior, tool and data access, and human approval points.
• Connect agent capabilities to CI/CD and DevSecOps pipelines for repeatable development, testing, deployment and lifecycle management.
• Work with platform engineers to run AI workloads on Kubernetes and OpenShift.
• Work with commercial AI and technology partners to bring their capabilities into the wider engineering ecosystem.
• Troubleshoot problems across models, frameworks, APIs, data sources, networking, identity and infrastructure.
• Develop reusable patterns for deploying, configuring, observing, evaluating and managing agents.
• Write up architectures, integration patterns, agent specifications, evaluation results and operating procedures.
• Spot recurring AI engineering problems that can be standardized, automated or turned into reusable products.
• Track fast-moving agentic AI technology and judge how well it suits secure government environments.

Requirements

• You hold an active Top Secret clearance.
• You have hands-on experience building applications or systems with large language models and generative AI.
• You have designed and implemented AI agents or agentic workflows.
• You program well in Python, or another modern language.
• You have integrated AI systems with APIs, enterprise applications, databases and external tools.
• You have worked with at least one agent framework or orchestration approach.
• You understand RAG, embeddings, vector databases, tool calling, structured outputs and prompt engineering.
• You have developed software in a Git-based, automated CI/CD environment.
• You can prototype, test, debug and iterate quickly when requirements are unclear.
• You understand software engineering basics, including testing, version control, APIs, debugging and system design.
• You work effectively with platform engineers, government staff and several technology vendors.
• Preferred: experience with MCP and tool-based agent architectures.
• Preferred: experience with multi-agent systems and agent orchestration.
• Preferred: experience with foundation model ecosystems such as OpenAI, Anthropic, Google, NVIDIA or comparable.
• Preferred: experience with NVIDIA NIM, NeMo or similar AI inference and model-serving technologies.
• Preferred: experience with Red Hat OpenShift AI or Kubernetes-based AI platforms.
• Preferred: experience with agent evaluation and observability frameworks.
• Preferred: experience implementing AI guardrails, policy enforcement, human-in-the-loop workflows or AI safety controls.
• Preferred: experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel or comparable frameworks.
• Preferred: experience with RAG pipelines, vector databases, knowledge graphs and enterprise search.
• Preferred: experience deploying AI systems in Secret or TS/SCI environments.
• Preferred: experience with disconnected or air-gapped AI environments.
• Preferred: experience integrating AI into developer or DevSecOps workflows.
• Preferred: familiarity with GitLab, Jira, Confluence, Xacta or similar engineering and compliance tools.
• Preferred: experience with GPUs, model serving, inference optimization or AI infrastructure.
• Preferred: familiarity with UDS, Zarf, Pepr, Iron Bank or similar secure software delivery technologies.
• Preferred: experience taking AI prototypes into repeatable, production-ready capabilities.
• Preferred: ability to evaluate new AI technologies quickly and make pragmatic build, buy or integrate decisions.

Conditions

The role is remote, based in the USA.

Pay depends on experience and national benchmarking data. The range is $148,750 - $201,250 USD and applies across all US locations.

Candidates who do not meet every preferred qualification are still encouraged to apply.

Benefits

• Medical, dental and vision coverage, with premiums 100% company paid.
• Health savings account.
• Life insurance and disability insurance.
• 401k retirement plan.
• Company stock options.
• Home office budget.
• Flexible time off for full-time staff, plus all federal holidays, one week for Thanksgiving, and two weeks for Christmas and New Year's.
• Paid parental leave.
• Reimbursement for approved training and subscriptions.
• Conferences, with travel, lodging and fees covered.