Senior Manager, AI Engineer.

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.
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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