Senior Manager, Application & Machine Learning Engineer

Senior Manager, Application & Machine Learning Engineer
Role Overview
The Coca-Cola Company's Technology organization is in the midst of a digital transformation that enables employees to leverage world-class technology to better connect our products, customers, and business operations across the globe. Our scale and complexity create a unique opportunity to solve challenging problems through data, machine learning, and AI-driven solutions.

The Senior Manager, Application & Machine Learning Engineering is a hands-on individual contributor responsible for developing and supporting production ML and AI applications using Python, Microsoft Azure, and Databricks. The role combines application engineering with forecasting and optimization model development, GenAI, and agentic AI to support commercial decisions across Coca-Cola's global business.

In partnership with product managers, engineers, architects, and business stakeholders, this role translates pricing, promotion, revenue growth, profitability, and operational needs into applications built on enterprise data. Responsibilities span model development, application integration, deployment, and ongoing evaluation. Success is measured by solution reliability, model and agent effectiveness, and demonstrable business outcomes.

What You'll Do
Build Production AI & Machine Learning Solutions
Develop forecasting, predictive, and recommendation models using enterprise datasets. Formulate optimization models with defined business objectives, decision variables, and constraints. Build GenAI applications and agentic workflows using LLMs, prompt engineering, and RAG, and integrate them with enterprise applications. Evaluate model outputs and agent recommendations for accuracy, grounding, and usefulness in business decisions.

Drive Production Delivery
Deliver and support solutions throughout development, deployment, monitoring, evaluation, and continuous improvement. Monitor model performance and data drift, evaluate retrieval and agent outputs, and address production issues. Ensure solutions meet enterprise standards for security, privacy, reliability, and responsible AI. Engineering Excellence
Develop reusable Python components, APIs, microservices, and distributed services for ML and AI applications. Implement automated testing, CI/CD, deployment automation, and observability.

Establish engineering patterns that improve delivery speed, quality, maintainability, and scalability. Collaboration & Influence
Translate business requirements into technical approaches and measurable success criteria. Explain model assumptions, outputs, and limitations to technical and non-technical stakeholders. Resolve conflicting requirements and recommend feasible scope and delivery timelines.

Required Skills
Experience
7+ years of experience in software engineering, machine learning engineering, AI engineering, data science, or related technical disciplines
3+ years delivering ML/AI solutions in production, with direct responsibility for development, deployment, and support
Experience building AI decision-support solutions for global organizations in commercial, supply chain, pricing, or profitability use cases
Technical Experience:
Developing production-quality Python software
Using Microsoft Azure and Databricks in production, including data processing and integration of ML/AI workflows with applications
Preparing and processing large-scale enterprise datasets for ML/AI solutions
Experience designing, developing, and supporting:
Cloud-native applications, APIs, microservices, and distributed systems
MLOps practices for model deployment, monitoring, and lifecycle management
CI/CD pipelines, automated testing, deployment automation, and application observability
Machine Learning & AI Experience:
Developing, evaluating, and deploying machine learning models, including forecasting models
Formulating optimization models that translate business objectives into decision variables and constraints
Building production GenAI applications using LLMs, prompt engineering, and RAG
Building and deploying AI agents and workflows using at least one orchestration framework or managed agent service, such as:
LangGraph
Semantic Kernel
AutoGen
Azure AI Agent Service
OpenAI Agents SDK
Business & Technical Collaboration:
Translating business requirements into technical solutions and communicating model outcomes and limitations
Managing competing priorities, resolving ambiguity, and recommending practical delivery approaches

Preferred Skills
Data Platforms
Experience with Spark/PySpark and Azure Data Services
Experience with data warehousing and both structured and unstructured enterprise datasets
Specialized Modeling & AI
Experience with XGBoost, Gurobi, or comparable modeling libraries and optimization solvers
Experience developing simulation models
Experience developing multi-agent architectures