Senior Manager, AI Engineer

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 Dev Ops 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 ure 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…