Finance Artificial Intelligence

Job DescriptionWhat’s on the menu?Own the deployment pipeline for Finance AI agents and tools — taking validated, production-ready components from the Infrastructure & Agents team and deploying them reliably to Finance business users across all pillarsBuild and own the Finance AI enablement layer — user guides, onboarding materials, training content, and the documentation that lets Finance teams use AI tools without requiring the Architecture team in the roomOwn the Finance AI intake and release process — manage the queue of deployment requests from domain pillars, sequence releases, and coordinate with the Infrastructure & Agents Manager on readiness gatesBuild and maintain the Finance AI knowledge base — architecture decision records, prompt libraries, agent catalog, and the living documentation of what is deployed, where, and at what versionTrack and report adoption metrics across deployed Finance AI tools — usage, active users, error rates, and escalations — and feed that signal back to the Architecture Lead and domain pillar teamsSupport change management for Finance teams adopting AI tools — work with domain pillar leads to identify adoption blockers and build targeted interventions that are not just more training decksManage and develop one Senior Analyst — set delivery standards, run reviews, and build someone who can own deployment tracks independentlyCoordinate with IT, Information Security, and Internal Audit on deployment governance — access controls, data classification, and the change management artifacts auditors will ask forRecipe for Success — apply now if this sounds like you!I have +5 years of experience in data engineering, analytics, or technical program management — with at least 1–2 years deploying or scaling AI/ML or analytics tools to business usersI am technical enough to understand what I am deploying — I can read Snowflake pipelines, Python code, and agent configurations — even if I am not the primary builderI have experience managing deployment pipelines, release processes, or MLOps workflows for data or AI products in an enterprise environmentI know how to drive adoption of technical tools with non-technical users — I have built enablement content that people actually use, not just documentation that gets ignoredI understand data governance, access controls, and the audit documentation requirements of deploying AI in a SOX-regulated finance environmentI have managed or mentored at least one person — I develop people through the work, not through separate development conversationsI translate between technical teams and finance business users without losing meaning in either directionI have a Bachelor’s degree in Computer Science, Engineering, Finance, or a related fieldLocation(s)Mexico City - Antara Tower A - 5th Floor - Local Office Kraft Heinz is an Equal Opportunity Employer – Underrepresented Ethnic Minority Groups/Women/Veterans/Individuals with Disabilities/Sexual Orientation/Gender Identity and...