Workforce Reinvention – AI Engineer

About the Role

The Workforce Reinvention – AI Engineer will help scale modular tools, intelligent workflows, and production-grade Retrieval-Augmented Generation (RAG) systems that support internal business operations. This person will serve as a pragmatic builder who consistently selects the most efficient solution for each challenge, whether through system integrations, lightweight scripting, reusable automation components, or custom AI agents.

The role will focus initially on maturing existing AI proofs-of-concept into reliable production systems while establishing engineering rigor through Test-Driven Development (TDD), CI/CD automation, infrastructure-as-code practices, and operational monitoring.

Success in this role will require strong partnership with internal “AI Champions” across departments to create reusable automation capabilities that empower teams to safely self-serve and scale intelligent workflows across OpenSesame.

Performance Objectives

30 Days — Onboarding, Context, and First Contribution

• Develop a comprehensive understanding of the current Workforce Reinvention architecture, codebase, deployment pipeline, and existing AI proofs-of-concept while building strong working relationships with AI Champions in Sales, Marketing, and Finance.
• Within the first month, successfully contribute and deploy a small but meaningful workflow improvement, automation script, or enhancement to an active internal tool while demonstrating the ability to operate within the team’s TDD and CI/CD practices and release processes.

60 Days — Production Launch & Component Foundations

• Take ownership of deploying a production-grade RAG workflow or intelligent internal tool built from a previously validated concept, ensuring the solution is stable, maintainable, and valuable to internal stakeholders.
• Partner closely with AI Champions to identify and prioritize workflow bottlenecks while beginning to establish reusable modular automation blocks and standardized development patterns that form the foundation of a scalable enterprise component library.

90 Days — Engineering Rigor & Pipeline Optimization

• Lead improvements to the CI/CD pipeline that significantly reduce manual deployment steps, improve release reliability, and accelerate software delivery across automation initiatives.
• Begin implementing infrastructure-as-code practices using tools such as Terraform while introducing robust monitoring, logging, and observability standards that improve system reliability, track operational costs, and support a target uptime of 99.9% for critical internal automation APIs and workflows.

6 Months — The Scalable Automation Ecosystem

• Fully establish and maintain a reusable component library that enables rapid deployment of AI-driven and traditional automation workflows across multiple business units while empowering AI Champions to safely self-serve and scale departmental solutions independently.
• Create a sustainable operational model where engineering maintains stable infrastructure, automated testing eliminates operational toil, documentation supports non-technical users, and intelligent workflows become a dependable force multiplier across the organization.

What Success Looks Like

• Successfully turns experimental AI ideas into scalable production systems that improve operational efficiency across OpenSesame.
• Builds reliable, maintainable automation frameworks that internal teams can confidently use and expand on.
• Becomes a trusted technical partner to AI Champions and helps create a scalable automation ecosystem that increases productivity across the company.

You might notice we don’t list a traditional set of requirements or buzzwords here. That’s intentional.

We’re looking for proven examples from your career that show you can build brands, create scalable systems, and drive measurable marketing impact. When you look back a year from now, you’ll know you’ve elevated OpenSesame’s brand and strengthened its market presence.