AI Workflow Engineer | LATAM

Role overview This role sits inside a company-wide AI transformation program as the hands-on builder within the AI Program Office. Working alongside senior AI enablement leadership, the engineer prototypes, builds, and ships AI-powered workflow solutions that are deployed into business teams outside of engineering and go-to-market functions. It is ideal for someone who started in a business-facing role and layered on serious AI and automation skills, and is based in the LATAM region.

Responsibilities - Take scoped use cases and build working solutions including prompt layers, automation flows, lightweight agents, retrieval-augmented generation implementations, and API integrations - Build working prototypes quickly so business teams can see what is possible in days rather than weeks, then iterate based on real feedback - Translate documented workflow assessments into technical designs, select appropriate tools, and own builds through to deployable solutions - Maintain documentation clear enough for handoff, iteration, and audit by both engineers and the business owners who will live with the solution - Embed inside functions such as Finance, HR, Legal, Marketing Operations, and Customer Success to map current-state workflows and identify where AI creates real leverage - Build demos and proof-of-concepts that help qualify or disqualify use cases early, and contribute a builder's perspective to vendor and tool evaluations

Requirements - Background starting in a business-facing role such as finance, operations, marketing, customer success, or HR, with serious hands-on AI and automation skills layered on top - Hands-on experience building with AI tools, including prompt engineering, automation platforms such as Make, Zapier, n8n, or equivalents, API integrations, and working familiarity with retrieval-augmented generation architectures and agent frameworks - Demonstrated success taking a messy real-world process and shipping something that makes it faster or better, not just technically cleaner - Comfortable working with non-technical stakeholders, explaining builds without jargon and gathering requirements without a formal spec - Strong enough with code or no-code tools to ship solutions to a deployable state independently without needing an engineer - Ability to manage multiple active projects at different stages simultaneously and switch between tactical quick wins and longer-horizon strategic builds

Nice to have - Direct experience working in or closely with general and administrative, finance, legal, marketing operations, or customer success teams - Familiarity with AI governance basics such as data handling, personally identifiable information awareness, and when a security or privacy review is required - Experience working in a small program or startup environment wearing multiple hats with limited process overhead - Background in cloud infrastructure, SaaS, or storage companies - A portfolio, GitHub, or examples of AI workflows or automations previously built