Senior Manager, Application & Machine Learning Engineer

Job Description

Summary:

Senior Manager, Application & Machine Learning Engineer Role Overview You will build the models, optimizers and agents behind pricing, pack and promotion decisions across Coca-Cola's markets — from XGBoost and Gurobi to Lang Graph — on Databricks and Azure, working with some of the largest commercial datasets in consumer goods. The role sits in the Product Engineering team of Coca-Cola's Global Digital Network, in a small, senior team that turns business questions from operating units and bottlers into secure, data-driven agentic solutions with executive-ready insight, increasingly delivered inside Microsoft Teams.

This is a hands‑on senior engineering role, not a people‑management one. Most of your week is spent building and shipping — writing Python, training and scoring models, engineering prompts, building agents — and what you ship is used by the people who run the business.

Build and ship production machine learning, optimization and forecasting models on Databricks and Azure — on very large structured and unstructured datasets — that feed commercial decisions on pricing, packs and promotions. Design and deliver generative AI and agentic solutions — RAG, tool-calling agents and multi-agent workflows on frameworks such as Lang Graph, Semantic Kernel or Azure AI Agent Service — that give a category or brand manager a grounded, auditable answer in Teams.

Work directly with product managers, data engineers, architects and business leaders in operating units and bottlers to turn an ambiguous question into a model, an optimization and an agent that explains its answer. Own your solutions in production: evaluation of model and LLM output, monitoring, MLOps and lifecycle management, and compliance with enterprise security, privacy and responsible AI standards. Set the engineering patterns the team reuses — code quality, testing, CI/CD, observability — so that delivery gets faster and safer with each release.

Help shape a team that is being built now: your choices of algorithms, frameworks and practices become the standard.

• 7+ years in software, application or machine learning engineering or data science, including 3+ years delivering machine learning or AI solutions in production.
• Python at production quality: you write code that others ship and maintain.
• Applied machine learning and data science on very large datasets: model development, deployment and MLOps.
• One to two years of demonstrable prompt engineering, and at least one generative AI application delivered to production with real users.
• At least one agent built and shipped on a recognized framework (Lang Graph, Semantic Kernel, Auto Gen, OpenAI Agents SDK or Azure AI Agent Service), including retrieval-augmented generation.
• Production software practices: APIs and services, CI/CD, automated testing and observability.

• Hands‑on Microsoft Azure/AWS Databricks, including Spark / PySpark and Azure data services.
• XGBoost, forecasting and simulation models.
• Optimization modeling with Gurobi or open-source equivalents, and operations research techniques.
• Multi‑agent architecture and LLM evaluation frameworks.
• Pricing, promotion, profitability, forecasting or supply chain use cases, ideally in consumer goods or for a global organization.
• R, in addition to Python.

Scale that is hard to find: terabyte-scale commercial data from markets on four continents, and decisions that move volume and profit in the field. A modern stack and the mandate to use it A small senior team being built now, with three roles opening together: you set the standards rather than inherit them. Direct access to the business leaders whose decisions your work informs, and the backing to say what is and…