Senior Manager, AI Engineer

Coca-Cola; Atlanta (GA); On-site; USD 152,000 - 178,000; Full time

Job summary

The Coca-Cola Company seeks an experienced AI/ML engineer to design, build, deploy, and operate production‑grade AI solutions for its digital product portfolio in cloud environments. You will prototype, iterate, and productionize domain‑specific AI agents and GenAI solutions for information gathering, insight generation, and intelligent action.

You will implement agents using MCP and A2A standards to securely connect with enterprise data, tools, and external systems, and produce production‑grade

Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related field.
• 3–5+ years hands-on AI/ML engineering experience taking models to production.
• Proficiency in Python; knowledge of Java or C++.
• Experience deploying LLM-powered and agentic AI applications in production.

Responsibilities

• Prototype and productionize domain AI agents and GenAI solutions.
• Build agents using MCP and A2A standards for secure data & tools integration.
• Deliver production‑grade AI code in Python and other languages.
• Optimize models and inference pipelines for performance and cost.
• Follow Coca-Cola engineering standards for quality, security, and observability.
• Deploy AI agents on Azure and maintain MLOps pipelines.
• Ensure high availability and end-to-end observability for AI products.
• Collaborate with Application and Data Engineering squads to embed AI in product workflows.

Skills

Python

Java

C++

AI/ML engineering

LLM AI

Education

Bachelor’s or Master’s degree in CS/AI/ML

Tools

LangChain

LangGraph

CrewAI

Semantic Kernel

MCP

A2A

Azure

AWS

GCP

Job description

Design, build, deploy, and operate production-grade AI solutions and AI agents across Coca-Cola’s digital product portfolio in cloud environments.

Responsibilities

• Prototype, iterate, and take to production domain‑specific AI agents and GenAI solutions for information gathering, insight generation, and intelligent action
• Design and implement agents using interoperability standards including Model Context Protocol (MCP) and Agent-to-Agent (A2A) to securely connect agents with enterprise data, tools, and external systems
• Produce production‑grade, well‑tested, maintainable AI code in Python and other relevant languages
• Optimize AI models and inference pipelines for performance, reliability, and cost efficiency at scale
• Follow The Coca‑Cola Company engineering standards for quality, security, and observability
• Deploy, monitor, and optimize AI agents and models on Azure cloud infrastructure
• Build and maintain MLOps pipelines covering 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 by collaborating with Application Engineering and Data Engineering squads to embed AI outputs into product workflows, APIs, and user‑facing features
• Implement AI observability and telemetry for agents and LLM applications, including tracing, reasoning paths, token consumption, latency, cost, output quality, and guardrail violations
• Build agent evaluation frameworks using evaluation pipelines, harnesses, benchmark datasets, regression tests, and automated quality scoring for accuracy, safety, and business performance
• Engineer trusted AI context using retrieval pipelines with enterprise semantic layers, knowledge graphs, vector search, and business ontologies
• Implement AI safety and runtime controls, including policy enforcement, human‑in‑the‑loop workflows, autonomy thresholds, prompt injection defenses, and secure tool execution
• Design and orchestrate multi‑agent systems for coordinated planning, reasoning, tool execution, and human collaboration
• Operate LLM and agent applications in production using modern LLMOps and AgentOps practices such as prompt versioning, evaluation, experimentation, routing strategies, cost optimization, and lifecycle management
• Support enterprise digital twin capabilities by integrating operational, commercial, and enterprise data into simulations, predictions, and decision‑support workflows
• Collaborate in agile engineering teams through technical design discussions, code reviews, testing, and delivery with Technical Leads, software engineers, data engineers, and other AI Engineers
• Participate in sprint planning, backlog refinement, and collaborative problem‑solving to deliver high‑quality engineering outcomes
• Stay current with advances in AI, machine learning, and Generative AI and apply relevant improvements to existing and new solutions
• Conduct rigorous testing and validation for reliability, accuracy, and explainability of AI agents and outputs
• Share knowledge internally and contribute to code reviews and engineering best practices

Requirements

• 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, MCP, A2A, or similar
• Experience deploying and operating AI and ML solutions on cloud infrastructure with Azure strongly preferred; AWS or GCP acceptable
• 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 model and prompt versioning, evaluation, experimentation, routing strategies, observability, cost optimization, and production lifecycle management
• Strong software engineering fundamentals: API design, testing, version control, CI/CD, containerization with Docker and Kubernetes, and model deployment and monitoring
• Experience working in Agile delivery environments, including sprint execution, code review practices, and cross‑squad collaboration
• Experience implementing responsible AI practices such as runtime guardrails, AI safety controls, model explainability, data privacy, and secure agent execution
• Experience implementing AI observability and telemetry (tracing, reasoning diagnostics, token consumption monitoring, latency, cost, output quality, and runtime performance)
• Strong analytical and problem‑solving skills with the ability to work with Tech Leads and Product teams to translate business requirements into well‑scoped AI solutions
• Excellent communication skills to explain AI products and trade‑offs to technical and non‑technical stakeholders

Tech Stack

• Python, Java, C++, LangChain, LangGraph, CrewAI, Semantic Kernel
• Model Context Protocol (MCP), Agent-to-Agent (A2A)
• Azure, AWS, GCP
• Vector databases, embeddings, Retrieval‑Augmented Generation (RAG), GraphRAG
• RESTful APIs
• MLOps, LLMOps, AgentOps, Docker, Kubernetes

Location and Compensation

• Location: Atlanta, GA (onsite)
• Salary: USD 152,000 - 178,300 per year (United States)
• Annual incentive: reference value 15%
• Long‑term incentive: reference value 0 - 20%
• Travel required: 0% - 25%
• Relocation provided: No
• Job posting end date: September 27, 2026

Work Authorization

• The Coca‑Cola Company will not offer sponsorship for employment status (including H1‑B visa status)
• Applicants must be currently authorized to work in the United States on a full‑time basis and must not require sponsorship to continue to work legally in the United States

We are taking deliberate action to nurture an inclusive culture grounded in our company purpose to refresh the world and make a difference.