Manager Machine Learning Platform - Bees Data

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

• →Lead a team responsible for building and scaling BEES’s Machine Learning Platform, enabling ML, data, product, and engineering teams to develop, deploy, operate, and govern production ML systems with speed, reliability, and quality.
• →Translate BEES’s ML strategy into a clear platform roadmap, balancing business priorities, technical leverage, reliability, developer experience, cost efficiency, and long-term architecture.
• →Own execution for a broad ML platform domain, with the ability to lead across MLOps, model development, model serving, infrastructure, reliability, observability, governance, and developer experience.
• →Manage, coach, and grow a team of ML platform engineers, creating a high-performance environment with strong ownership, clear priorities, technical excellence, and continuous development.
• →Drive end-to-end delivery of reusable platform capabilities, including ML pipelines, experiment tracking, model registries, feature and data workflows, training infrastructure, deployment systems, inference services, monitoring, alerting, and governance workflows.
• →Partner closely with ML engineers, data engineers, platform engineers, product managers, security, infrastructure, and business teams to identify platform needs and turn them into scalable solutions.
• →Establish engineering standards and paved paths for building production ML systems, including project structure, CI/CD, testing, versioning, reproducibility, model evaluation, release management, rollback strategies, documentation, and operational readiness.
• →Improve developer productivity by building internal tools, frameworks, SDKs, templates, documentation, and automation that reduce friction across the ML lifecycle.
• →Ensure ML systems are production-ready, observable, reliable, secure, compliant, and cost-efficient across training, deployment, inference, monitoring, and continuous improvement.
• →Drive operational excellence through SLOs, SLAs, incident management, monitoring, capacity planning, cost management, runbooks, and post-incident learning.
• →Make thoughtful tradeoffs between speed, platform standardization, flexibility, technical debt, security, governance, and business impact.
• →Define and track success metrics for the platform, such as adoption, deployment frequency, time to production, model reliability, inference latency, training efficiency, infrastructure cost, incident reduction, and developer satisfaction.
• →Represent the ML Platform team in technical and strategic discussions, influencing architecture, standards, and roadmap decisions across BEES.

• Bachelor’s degree in computer science, engineering, mathematics, or another quantitative field, or equivalent practical experience. A master’s degree is a plus.
• Experience managing or technically leading engineering teams that build production ML platforms, data platforms, developer platforms, distributed systems, or cloud infrastructure.
• Strong understanding of the ML lifecycle, including data preparation, feature engineering, experimentation, training, evaluation, packaging, deployment, serving, monitoring, retraining, and governance.
• Ability to lead across multiple ML platform disciplines, including MLOps, model development workflows, model serving, infrastructure, reliability, observability, and platform governance.
• Strong systems thinking across scalability, reliability, latency, availability, security, cost, maintainability, and developer experience.
• Experience translating broad strategy into technical roadmaps, execution plans, measurable outcomes, and clear priorities for engineering teams.
• Technical credibility with modern ML platform and cloud-native technologies, such as Python, SQL, PySpark, Kubernetes, Databricks, Terraform, Azure DevOps or Git-based CI/CD, Azure Cloud, model registries, feature stores, ML pipelines, and inference platforms.
• Familiarity with ML frameworks, libraries, and serving technologies such as PyTorch, TensorFlow, Scikit-learn, ONNX, BentoML, Kedro, Seldon, KServe, Triton Inference Server, Ray, Spark, or similar tools.
• Experience operating production systems with monitoring, logging, alerting, incident response, capacity planning, SLOs, SLAs, cost optimization, and continuous reliability improvement.
• Strong architecture judgment, with the ability to guide senior engineers, evaluate technical tradeoffs, and create standards that scale across teams.
• Excellent communication and stakeholder management skills, with the ability to explain technical strategy, risks, tradeoffs, and priorities to engineering teams, product leaders, executives, and partner organizations.

• You are motivated by building world-class ML platform capabilities that help teams deliver AI products reliably and efficiently at scale.
• You combine technical depth with strong people leadership, helping engineers grow while holding a high bar for execution and quality.
• You are comfortable with ambiguity and can turn broad platform needs into clear priorities, practical plans, and measurable outcomes.
• You think in terms of leverage: reusable systems, paved roads, strong documentation, automation, standards, and adoption across teams.
• You are pragmatic and know how to balance innovation with reliability, cost, security, compliance, and maintainability.
• You communicate clearly, build trust across functions, and create alignment between technical teams and business priorities.
• You continuously look for ways to improve systems, team health, engineering practices, and business impact.

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

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Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com