AI Workflow Engineer
$150,000–$200,000 year
RemoteUnited States
Job Summary
Design, implement, and operate agentic workflows across the development lifecycle, integrating planning, code generation, review, testing, and release with our GitOps and CI/CD infrastructure. Engineer retrieval-grounded, context-aware AI systems that produce verifiable results while establishing governance, guardrails, and human-in-the-loop checkpoints to ensure responsible adoption. Build developer experience by introducing modern AI integrations like coding agents and automated review tools, then formalize successful patterns into repeatable frameworks and internal platforms. Champion the practice by evangelizing AI adoption across the team and supporting a culture of continuous delivery and operational excellence. Join a newly forming AI engineering team inside a mature product organization dedicated to workforce readiness for the Department of Defense.
Required Qualifications
- 2–3+ years of hands-on experience building with AI/LLM systems in production settings
- 2+ years designing and operating agentic workflows and pipelines including multi-step agent orchestration, tool use, evaluation, and governance
- Experience embedded in modern development teams practicing GitOps and CI/CD
- Working knowledge of grounded AI patterns: RAG, context engineering, structured outputs, and evaluation/observability for LLM systems
- Solid software engineering fundamentals in JavaScript/TypeScript and/or Python
- Genuine alignment with continuous delivery culture the Allspaw/Hammond, Phoenix Project (Kim) school of small batches, fast feedback, blameless learning, and operational excellence
- A self-starter's bias for action: you see an opportunity to accelerate the team and you build it, measure it, and share it
- Strong communication skills you can explain an agentic architecture to an engineer and its ROI to an executive
- Must be authorized to work in the United States; role is US-remote
Desired Qualifications
- Experience with agent frameworks and protocols (e.g., LangGraph/LangMem, Claude Agent SDK, MCP, OpenAI Agents) and AI coding tools (Claude Code, Copilot, Cursor)
- Building developer platforms, internal tooling, or platform engineering / DevEx functions
- DORA-style delivery metrics and engineering analytics
- Familiarity with AI governance frameworks (NIST AI RMF) and secure SDLC practices
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