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.

• 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…