Backblaze External Website - AI Workflow Engineer | LATAM

• Build and Ship AI Workflow Solutions • Take scoped use cases from the Director, AI Enablement and build working solutions: prompt layers, automation flows, lightweight agents, RAG implementations, and API integrations that connect AI tools to the systems business teams already use • Build working prototypes quickly - the goal is to show a team what is possible in days, not weeks, then refine based on what actually lands • Translate a documented workflow assessment into a technical design, select the right tools for the job, and own the build through to a deployable solution • Maintain solution documentation that is clear enough for handoff, iteration, and audit - not just for engineers, but for the business owner who will live with it • Embed in Business Teams • Spend time inside the functions you are building for - Finance, HR, Legal, Marketing Ops, Customer Success, and others - understanding how the work actually flows before you redesign it • Run working sessions with team members to map current-state workflows, surface friction points, and identify where AI creates real leverage versus where it creates noise • Build credibility with non-technical stakeholders by showing them working solutions, not decks - and by being honest about what will and will not work • Support user acceptance testing and early adoption, troubleshooting issues close to launch and iterating based on real usage • Support the AI Program Office • Build demos and proof-of-concepts that help the Director, AI Enablement make the case for a use case - or disqualify it early before resources are committed • Contribute to vendor and tool evaluations with a builder's perspective: not just whether a tool claims to do something, but whether it actually works in the context of a specific workflow • Feed learnings from the build process back into the program - what worked, what did not, what should be standardized across functions • Work on tactical quick wins and longer-horizon strategic builds at the same time, switching contexts across active use cases as priorities shift • What Success Looks Like (First 90 Days) • You have completed at least one workflow assessment alongside the Director and produced a working prototype from it • At least one solution you built is in active use by a business team, with a named owner and feedback loop in place • You have worked inside at least two different business functions and understand their core workflows well enough to identify the next use case without being told • The Director, AI Enablement trusts you to own the build layer independently - scoping conversations happen together, execution happens with you driving • The Right Fit: • The Right Fit: • 2-4 years of experience that includes meaningful time working inside a business-facing function - operations, finance, marketing, customer success, HR, or similar - not just in Engineering or IT • Hands-on experience building with AI tools: prompt engineering, automation platforms (Make, Zapier, n8n, or equivalent), API integrations, and at least working familiarity with RAG architectures and agent frameworks • Proven ability to take a messy real-world process, understand it from the inside, and build something that makes it faster or better - not just technically cleaner • Comfortable working with non-technical stakeholders: you can explain what you are building without jargon, gather requirements without a formal spec, and manage expectations when a prototype needs three more iterations • Strong enough with code or no-code tools to get solutions to a deployable state independently - you do not need an engineer to ship your work • Able to manage multiple active projects at different stages without dropping anything - a quick win in HR this week does not mean the Finance automation waits • Genuinely curious about how companies work: the org chart, the incentives, the handoffs, the bottlenecks. This is what separates a good AI builder from a great one in a business context