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.
Required Skills
Experience
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 will not offer sponsorship for employment status (including, but not limited to, H1-B visa status and other employment-based nonimmigrant visas) for this position. Accordingly, all applicants must be currently authorized to work in the United States on a full-time basis and must not require The Coca-Cola Company's sponsorship to continue to work legally in the United States.
Skills
Budget Management, Communication, Data Analytics, DOT Regulations, Group Problem Solving, JDA (Inactive), Microsoft Office, Microsoft Power Business Intelligence (PBI), Oracle Transportation Management, SAP Manufacturing Execution (SAP ME), Supply Chain, Tableau (Software), Transportation Logistics, Transportation Management Systems (TMS), Transportation Planning
Pay Range
United States: 152,000 - 178,300 USD

Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage
15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s)
United States of America
City/Cities
Atlanta
Travel Required
00% - 25%
Relocation Provided
No
Job Posting End Date
November 15, 2026
Our Purpose and Growth Culture
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.
