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Job Description Summary:
Role Overview
As part of Product & Engineering team within the Global Digital Network, the Senior Manager, AI Engineer will help advance Coca-Cola’s transformation into a digital-first, data-driven enterprise. We are seeking an AI Engineer to design, build, and deploy production-grade AI solutions across The Coca-Cola Company’s digital product portfolio. This is a hands-on engineering role at the frontier of applied AI, responsible for taking business requirements or user needs from prototype to production, developing domain-specific AI agents, and integrating cutting-edge GenAI and agentic frameworks into enterprise platforms.
The ideal candidate is a skilled, curious AI practitioner who writes high-quality code, thrives in fast-moving agile squads, and has deep hands-on experience building and deploying AI systems or products in cloud environments. You are as comfortable discussing model architecture with a data scientist as you are reviewing a CI/CD pipeline with a DevOps engineer, and you bring the engineering discipline to turn promising AI prototypes into reliable, production-ready products.
What You’ll Do for Us
Develop and deploy AI agents and GenAI solutions: prototype, iterate, and take to production domain-specific AI agents capable of information gathering, insight generation, and intelligent action. Design and implement AI agents using open interoperability standards such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) to securely connect agents with enterprise data, tools, and external systems while enabling coordinated multi-agent workflows across business domains
Write and optimize production-grade AI code: produce high-quality, well-tested, maintainable code in Python and other relevant languages. Optimize AI models and inference pipelines for performance, reliability, and cost efficiency at scale. Ensure all code adheres to The Coca-Cola Company’s engineering standards for quality, security, and observability
Deploy and operate AI solutions on cloud infrastructure: deploy, monitor, and optimize AI agents and models on Azure cloud infrastructure. Build and maintain MLOps pipelines covering model training, versioning, inference, and CI/CD. Ensure high availability, scalability, and end-to-end observability for AI products in production
Integrate AI capabilities into enterprise platforms: collaborate with Application Engineering and Data Engineering squads to embed AI outputs into product workflows, APIs, and user-facing features; work cross-functionally to translate data science prototypes into robust, production-ready applications; and ensure seamless integration of AI components with existing enterprise data platforms and business systems
Implement AI observability and telemetry: implement runtime observability for AI agents and LLM applications, including tracing, reasoning paths, token consumption, latency, cost, output quality, and guardrail violations to ensure production reliability
Build agent evaluation frameworks: design agent evaluation pipelines, develop evaluation harnesses, benchmark datasets, regression tests, and automated quality scoring to continuously assess agent accuracy, safety, and business performance
Engineer enterprise AI context: design retrieval pipelines using enterprise semantic layers, knowledge graphs, vector search, and business ontologies to ground AI agents in trusted enterprise context and improve response quality
Implement AI safety and runtime controls: configure runtime AI controls including policy enforcement, human-in-the-loop workflows, autonomy thresholds, prompt injection defenses, and secure tool execution for enterprise AI agents
Design multi-agent systems: design and orchestrate multi-agent systems that coordinate planning, reasoning, tool execution, and human collaboration across complex enterprise workflows
Operate AI applications in production: manage prompt versioning, evaluation, experimentation, routing strategies, cost optimization, and the production lifecycle for LLM- and agent-powered applications using modern LLMOps and AgentOps practices
Support digital twin capabilities: develop AI capabilities that support enterprise digital twins by integrating operational, commercial, and enterprise data into intelligent simulations, predictions, and decision-support workflows
Collaborate effectively within agile engineering teams: work closely with Technical Leads, software engineers, data engineers, and fellow AI Engineers to design, build, test, and deliver AI capabilities. Contribute to sprint planning, backlog refinement, technical design discussions, code reviews, and collaborative problem-solving to ensure high-quality engineering outcomes
Maintain technical currency and drive continuous improvement: stay current with advances in AI, machine learning, and Generative AI and integrate relevant developments into existing and new solutions; conduct rigorous testing and validation to ensure reliability, accuracy, and explainability of AI agents and outputs; and contribute to internal knowledge sharing, code reviews, and engineering best practices
Requirements & Qualifications
Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Software Engineering, or a related technical field
3 to 5+ years of hands-on experience in AI or ML engineering with a demonstrated track record of taking AI models and solutions from development to production
Strong proficiency in Python with working knowledge of additional languages such as Java or C++
Experience building and deploying LLM-powered and agentic AI applications in production using frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, Model Context Protocol (MCP), Agent-to-Agent (A2A), or similar
Experience deploying and operating AI and ML solutions on cloud infrastructure with Azure strongly preferred and AWS or GCP also acceptable
Experience with vector databases, embeddings, Retrieval-Augmented Generation (RAG), GraphRAG, semantic search, or context engineering
Experience developing RESTful APIs or integrating AI capabilities into enterprise applications
Experience with MLOps, LLMOps, or AgentOps practices including model and prompt versioning, evaluation, experimentation, routing strategies, observability, cost optimization, and production lifecycle management
Strong software engineering fundamentals including API design, testing, version control, CI/CD, containerization using Docker and Kubernetes, and model deployment and monitoring for AI systems
Experience working in Agile delivery environments including sprint execution, code review practices, and cross-squad collaboration
Experience implementing responsible AI practices including runtime guardrails, AI safety controls, model explainability, data privacy, and secure agent execution
Experience implementing AI observability and telemetry including tracing, reasoning diagnostics, token consumption monitoring, latency, cost, output quality, and runtime performance for production AI applications
Strong analytical and problem-solving skills with the ability to work with Technical Leads and Product teams to translate business requirements into well-scoped AI solutions
Excellent communication skills with the ability to explain AI products and trade-offs clearly to both technical and non-technical stakeholders
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 PlanningPay Range:
United States: 152,000 - 178,300 USDBase 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:
15Annual 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 AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
NoJob Posting End Date:
October 30, 2026Our 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.Skills Required
- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Software Engineering, or a related technical field
- 3 to 5+ years of hands-on experience in AI or ML engineering, taking AI models and solutions from development to production
- Strong proficiency in Python
- Working knowledge of Java or C++
- Experience building and deploying production LLM-powered and agentic AI applications
- Experience with AI frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, MCP, or A2A
- Experience deploying and operating AI or ML solutions on cloud infrastructure; Azure strongly preferred
- Experience with vector databases, embeddings, RAG, GraphRAG, semantic search, or context engineering
- Experience developing RESTful APIs or integrating AI capabilities into enterprise applications
- Experience with MLOps, LLMOps, or AgentOps, including versioning, evaluation, experimentation, observability, routing, cost optimization, and production lifecycle management
- Software engineering fundamentals including API design, testing, version control, CI/CD, Docker, Kubernetes, and AI model deployment and monitoring
- Experience working in Agile delivery environments with sprint execution, code reviews, and cross-squad collaboration
- Experience implementing responsible AI practices, runtime guardrails, safety controls, explainability, data privacy, and secure agent execution
- Experience implementing AI observability and telemetry for tracing, token consumption, latency, cost, output quality, and runtime performance
- Strong analytical and problem-solving skills
- Excellent communication skills for explaining AI products and trade-offs to technical and non-technical stakeholders
- Must be currently authorized to work full-time in the United States without employer sponsorship
The Coca-Cola Company Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about The Coca-Cola Company and has not been reviewed or approved by The Coca-Cola Company.
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Retirement Support — Retirement benefits are positioned as a standout, combining a 401(k) match with a company-funded cash-balance pension and an employee stock purchase plan match that together materially increase long-term package value.
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Healthcare Strength — Health coverage is described as broad and feature-rich, including national medical coverage plus specialized add-ons like virtual care, second opinions, oncology navigation, fertility support, and chronic-condition programs.
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Leave & Time Off Breadth — Time-off benefits are outlined with structured vacation accrual that increases with tenure and a holiday program that includes both set and floating days.
The Coca-Cola Company Insights
What We Do
The Coca-Cola Company (NYSE: KO) is a total beverage company, offering over 500 brands in more than 200 countries and territories. In addition to the company’s Coca-Cola brands, our portfolio includes some of the world’s most valuable beverage brands, such as AdeS soy-based beverages, Ayataka green tea, Dasani waters, Del Valle juices and nectars, Fanta, Georgia coffee, Gold Peak teas and coffees, Honest Tea, innocent smoothies and juices, Minute Maid juices, Powerade sports drinks, Simply juices, smartwater, Sprite, vitaminwater and ZICO coconut water.
