Senior ML Engineer (Agentic AI)

PURPOSE OF ROLE

Anheuser-Busch InBev (AB InBev)’s Supply Analytics is responsible for building competitive differentiated solutions that enhance brewery efficiency through data-driven insights. We optimize processes, reduce waste, and improve productivity by leveraging advanced analytics and AI-driven solutions.

As a Senior MLE, you will be responsible for driving the technical backbone of the internal ML platform—from model containerization and deployment at scale to architecting backend repos and pipelines that enable integration with the software/frontend layers. You will lead the end-to-end ML engineering lifecycle for edge deployments, ensuring high availability, observability, and scalability of the platform across 100+ brewery deployments globally.

KEY TASKS AND ACCOUNTABILITIES

• Own the backend engineering strategy for the platform, ensuring repos, pipelines, and modular components are structured for scalable integration with software applications and visualization layers.
• Lead the entire edge deployment lifecycle, from model training to deployment and monitoring on edge devices.
• Develop and maintain a scalable Edge ML pipeline that enables real-time analytics at brewery sites.
• Optimize and containerize models using Portainer, Docker, and Azure Container Registry (ACR) to ensure efficient execution in constrained edge environments.
• Own and manage the GitHub repository, ensuring structured, well-documented, and modularized code for seamless deployments.
• Establish robust CI/CD pipelines for continuous integration and deployment of models and services.
• Implement observability layers for data, feature, inference, and pipeline health (e.g., logging, monitoring, alerting), ensuring reliable and quick feedback loops.
• Ensure compliance with security and governance best practices for data and model deployment in edge environments.
• Collaborate with product and software teams to ensure backend architecture seamlessly integrates with frontend and operator-facing tools.
• Mentor other team members and set technical standards and review processes for high-quality, scalable, and secure code delivery.
• You will also develop internal tools/utils that improve productivity of entire team.

QUALIFICATIONS, EXPERIENCE, SKILLS

Education:

• Academic degree in, but not limited to, Bachelors or master's in computer application, Computer science, or any engineering discipline.

Experience:

• 5+ years of experience developing scalable and high-quality ML models and backend ML engineering infrastructure.
• Strong problem-solving skills with an owner’s mindset—proactively identifying and resolving bottlenecks.

Technical/Functional Skills:

Mandatory Skills:

• Python (pandas, NumPy, SciPy, scikit-learn, TensorFlow) - Expert
• GitHub, Docker, CI/CD Pipelines – Expert
• ML Model Design & Deployment– Expert
• Model Deployment – Expert
• Azure Cloud Architecture for ML– Advanced
• Containerization & Edge Deployment (Portainer, Kubernetes, ACR) – Advanced
• Observability (Logging, Monitoring, Alerting) – Intermediate
• Databricks (Workflows, Cluster Creation, Repo Management) – Intermediate
• Unit Testing – Intermediate

Preferred (Good to have) Skills:

• DevSecOps & Automation – Intermediate
• Computer Vision / Edge Vision Deployments – Beginner
• Multi-repo or Monorepo ML Architecture – Intermediate
• Real-time Analytics & Edge AI Deployments – Intermediate
• Open-source ML Tooling Contributions – Beginner

Behavioral Skills:

• You take full ownership of your projects & understand end-to-end expectations.
• You demonstrate thought leadership at work, assert ideas & influence overall direction of solutions.
• Collaborate with team members, share selflessly & improve the quality of code, models etc.
• Ability to simplify communicating the output of your work for business, create compelling documentation or any artifacts that connects business to the solutions.
• A drive to build platforms that power real business impact at scale.