AI Workflow Engineer at Ultimate Knowledge

, Florida, United States -

Full Time

Start Date

Immediate

Expiry Date

16 Oct, 26

Salary

200000.0

Posted On

18 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Workflow Engineering, Agentic SDLC, LLM Production, Multi-step Agent Orchestration, RAG, GitOps, CI/CD, JavaScript, TypeScript, Python, Angular, React, Express, Context Engineering, AI Governance, Evaluation Harnesses

Industry

Education

Description

Build the Agentic Software Factory AI is rewriting how software gets built and most organizations are still bolting chatbots onto yesterday's process. We're doing something different. UKi is looking for an AI Workflow Engineers to architect our agentic SDLC from the ground up: formalized pipelines where AI agents plan, build, test, review, and ship alongside a core team of skilled senior US-based engineers, with the governance, grounding, and measurement to prove it works. This isn't a research role and it isn't prompt tinkering. You'll be designing the delivery system itself the workflows, guardrails, and feedback loops that let a high-performing team move dramatically faster without sacrificing the engineering excellence we practice every day. Your work will directly accelerate a mission that matters: delivering workforce readiness to the nation's cyber warfighters. Agentic development is the biggest shift in how software gets built since continuous delivery itself. Very few teams get to design and own that transformation from the ground up, backed by senior engineers who want it to succeed, in service of a mission that protects the people defending the nation. If you've been waiting for the role where AI engineering meets real delivery discipline and real impact, this is it. About UKi Since 1999, Ultimate Knowledge Institute has set the standard for cybersecurity training and readiness for the Department of Defense, federal agencies, and Fortune 500 companies. Today we're delivering a platform that builds and demonstrates true workforce and work-role readiness. Our platform identifies individual expertise gaps and builds adaptive, custom training plans to close those gaps for Federal U.S. Government, Defense, Intelligence Community, State/Local, and commercial customers. You'll be joining a newly forming AI engineering team inside a mature product engineering team, which means real users, real stakes, and a green field for how AI transforms the way we build. What You'll Do • 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. Why Now Agentic development is the biggest shift in how software gets built since continuous delivery itself — and very few teams get to design that transformation from a blank page, backed by senior engineers who want it to succeed, in service of a mission that protects the people defending the nation. If you've been waiting for the role where AI engineering meets real delivery discipline and real impact, this is it. Required: • 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 (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. • Must be authorized to work in the United States; role is US-remote. Nice to Have • 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. Our Tech Environment Angular and React frontends on Express/Node.js web applications, delivered through GitOps and automated CI/CD pipelines. We practice continuous delivery: deploy often, automate everything, learn from every incident. You'll have wide latitude to introduce the agentic tooling and AI infrastructure this mission needs. Base salary range: $150,000–$200,000, commensurate with experience, plus benefits. This range reflects current market rates for mid-level AI/agentic engineering talent in US-remote roles.

Responsibilities

Architect and implement an agentic SDLC pipeline to automate planning, coding, testing, and release processes. Design grounded AI systems and establish governance guardrails to ensure reliable and secure AI-driven software delivery.