Senior Manager, Application & Machine Learning Engineer

The Senior Manager, Application & Machine Learning Engineering is an hands-on individual contributor who will build and deploy the machine learning, optimization, and AI solutions that support commercial decision-making across Coca-Cola’s global business. Working with large-scale enterprise datasets, you will develop forecasting models, predictive analytics solutions, optimization engines, and agentic AI applications that help business teams make better decisions around pricing, promotions, revenue growth, profitability, and operational performance.

You will work across traditional machine learning, generative AI, retrieval-augmented generation (RAG), and agentic AI, partnering with product managers, engineers, architects, and business stakeholders to transform complex business questions into scalable production solutions.

Success in this role requires both a strong machine learning foundation and practical experience building AI applications that are deployed, monitored, maintained, and continuously improved in production environments.

What You’ll Do

Build Production AI & Machine Learning Solutions

• Design, develop, deploy, and support machine learning, optimization, and AI solutions that drive commercial and operational decision-making
• Build predictive, recommendation, forecasting, and optimization models using large-scale structured and unstructured datasets.
• Develop generative AI applications using LLMs, prompt engineering, retrieval-augmented generation (RAG), and agentic AI frameworks.
• Build AI agents and decision-support experiences that provide grounded, explainable, and actionable recommendations to business users.

Drive Production Delivery

• Own solutions throughout the full lifecycle, from development through deployment, monitoring, evaluation, and continuous improvement.
• Monitor model and agent performance, identify drift, evaluate outputs, and continuously improve solution effectiveness.
• Ensure solutions meet enterprise standards for security, privacy, reliability, and responsible AI.

Engineering Excellence

• Write production-quality Python code and reusable engineering components.
• Build APIs, microservices, and distributed services that support AI and machine learning applications.
• Implement automated testing, CI/CD pipelines, deployment automation, observability, and operational readiness practices.
• Establish engineering patterns that improve delivery speed, quality, maintainability, and scalability.

Collaboration & Influence

• Explain complex technical concepts and model outputs to both technical and non-technical audiences.
• Translate business requirements into scalable technical solutions.
• Resolve ambiguity, recommend practical approaches, and influence stakeholders through technical expertise.

• 7+ years of experience in software engineering, machine learning engineering, AI engineering, data science, or related technical disciplines
• 3+ years of experience delivering AI and machine learning solutions in production environments
• Experience developing enterprise decision-support solutions that enable business and operational decision-making

Technical Experience:

• Python development in production environments
• Microsoft Azure and Databricks in production environments
• Azure Data Services
• Spark/PySpark
• Machine learning technologies such as XGBoost and optimization platforms such as Gurobi or comparable tools
• Large-scale enterprise datasets involving both structured and unstructured data

Experience designing, developing, and supporting:

• Cloud-native applications
• Distributed systems
• APIs and microservices
• Data-intensive applications and data warehousing solutions
• MLOps practices including model deployment, monitoring, lifecycle management, and observability
• CI/CD pipelines, automated testing, deployment automation, and operational readiness

Machine Learning & AI Experience:

• Developing, deploying, and supporting machine learning forecasting, predictive, recommendation, optimization, or other business decision-support models in production environments
• Building generative AI applications using:
• Large Language Models (LLMs)
• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• Building and deploying AI agents and agentic workflows using orchestration frameworks such as:
• LangGraph
• Semantic Kernel
• AutoGen
• Azure AI Agent Service
• OpenAI Agents SDK
• Similar agent frameworks

Business & Technical Collaboration:

• Translating business requirements into technical solutions
• Communicating technical decisions and model outcomes to both technical and non-technical stakeholders
• Managing competing priorities and recommending practical delivery approaches in ambiguous environments

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.