Remote AI Workflow Engineer - Agentic SDLC Architect

Ultimate Knowledge

Nashville (TN)

On-site

USD 150,000 - 200,000

Full time

4 days ago

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Job summary

Ultimate Knowledge Institute is seeking an AI Workflow Engineer to architect an agentic SDLC from the ground up, shaping formalized pipelines where AI agents plan, build, test, review, and ship alongside senior US engineers. You’ll design governance, guardrails, and feedback loops to accelerate delivery while maintaining engineering excellence.

You’ll join a mature product engineering environment delivering workforce readiness for DoD, federal agencies, and commercial customers, with a base

Qualifications

2–3+ years hands-on experience building with AI/LLM systems in production.
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 (required).
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; comfort working in and around our stack (Angular/React frontends on Express web applications).
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.

Responsibilities

Build our agentic SDLC pipeline. Design, implement, and operate agentic workflows across the development lifecycle - planning, code generation, review, testing, documentation, and release that is all integrated with our GitOps and CI/CD infrastructure.
Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling that removes friction and multiplies output.
Engineer grounded, trustworthy systems. Build retrieval-grounded and context-aware AI systems that produce reliable, verifiable results and design the evaluation harnesses that prove it.
Establish governance and guardrails. Define and implement the policies, human-in-the-loop checkpoints, audit trails, and security controls that let us adopt agentic AI responsibly in environments where trust is non‑negotiable.
Formalize and scale. Turn what works into repeatable frameworks, playbooks, and internal platforms that extend agentic acceleration beyond engineering to the whole organization.
Champion the practice. Evangelize, teach, and support AI adoption across the team; you'll be a force multiplier, not a silo.