Your responsibilities will include
Lead & Scale AI/ML Product Portfolio: Oversee the end-to-end lifecycle, design, and deployment of regional Machine Learning and Generative AI products (e.g., demand forecasting, inventory optimization, conversational commerce, and document/contract automation).
Cross-Functional Intake & Value Acceleration: Partner with business leaders across multidisciplinary areas (Supply Chain, Engineering, Legal, D2C, Trade) to translate business expectations and ideas into structured, high-ROI AI use cases.
Architect Enterprise AI & Multi-Agent Workflows: Guide the technical team in building scalable, secure AI solutions, including multi-agent systems, Vertex AI integrations, and API connections into enterprise platforms (e.g., Workspace, ServiceNow, VTEX).
Enforce MLOps & Production Governance: Ensure rigorous MLOps governance, model monitoring, CI/CD automation, and alignment with GCP and architecture standards, preventing model drift and technical debt.
Manage AI FinOps & Governance: Act as the technical gatekeeper for regional AI consumption costs (e.g., Gemini Enterprise licensing/usage) and ensure strict compliance with corporate security, privacy (LGPD/PIA/ISDR), and Red Data risk checks via the PULSE Committee.
Foster AI Culture & Maturity (AI Literacy): Champion AI enablement and adoption across different maturity levels in the organization, partnering with HR/Communications to advance the AI Literacy program.
Orchestrate External Vendors & Partners: Manage technical deliverables, SLAs, and development execution with third-party consultancies and tech partners.
Team Leadership & Talent Development: Mentor, recruit, and retain top-tier Data Science and AI talent, fostering an agile, high-performance, and business-oriented engineering culture.
Minimum requirements
Education: Bachelor’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, Statistics, or related fields.
Language Skills: Fluent/Advanced English (mandatory for global alignment and technical governance).
Systems/Technical Knowledge
Proven experience (5+ years) leading Data Science or AI engineering teams in corporate environments.
Deep hands-on expertise in Google Cloud Platform (GCP), specifically Vertex AI, BigQuery, Cloud Storage, and MLOps frameworks.
