Senior Manager, AI Engineer

The Coca-Cola Company•Atlanta, GA

5d•$152,000 - $178,300

About The Position

As part of the 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. This hands-on engineering role designs, builds, and deploys production-grade AI solutions across the company’s digital product portfolio. The role takes business requirements and user needs from prototype to production, develops domain-specific AI agents, and integrates generative AI and agentic frameworks into enterprise platforms. The position develops and operates AI agents and applications in cloud environments, builds MLOps and evaluation pipelines, integrates AI into enterprise products and data systems, and establishes observability, safety controls, and trusted enterprise context. It also supports enterprise digital-twin capabilities and contributes to agile engineering practices, technical collaboration, and continuous improvement.

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 AI or ML engineering experience, with a demonstrated track record of taking AI models and solutions from development to production.
Strong proficiency in Python and working knowledge of additional languages such as Java or C++.
Experience building and deploying production LLM-powered and agentic AI applications using frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, MCP, A2A, or similar.
Experience deploying and operating AI and ML solutions on cloud infrastructure; Azure is strongly preferred, with AWS or GCP also 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 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, Docker and Kubernetes containerization, and AI model deployment and monitoring.
Experience working in Agile delivery environments, including sprint execution, code reviews, 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.
Strong analytical and problem-solving skills, including the ability to work with technical leads and product teams to translate business requirements into well-scoped AI solutions.
Excellent communication skills and ability to explain AI products and trade-offs to technical and non-technical stakeholders.
Must be currently authorized to work full-time in the United States and must not require Coca-Cola sponsorship to continue working legally in the United States.

Nice To Haves

Azure cloud experience is strongly preferred; AWS or GCP experience is also acceptable.

Responsibilities

Prototype, develop, and deploy domain-specific AI agents and generative AI solutions, including coordinated multi-agent workflows using standards such as MCP and A2A.
Write, test, maintain, and optimize production-grade AI code and inference pipelines for quality, security, reliability, performance, and cost efficiency.
Deploy, monitor, and optimize AI agents and models on cloud infrastructure; build and maintain MLOps pipelines for training, versioning, inference, and CI/CD.
Integrate AI capabilities into enterprise workflows, APIs, user-facing features, data platforms, and business systems in collaboration with application and data engineering teams.
Implement runtime observability and telemetry for AI agents and LLM applications, including tracing, reasoning paths, token consumption, latency, cost, output quality, and guardrail violations.
Build agent evaluation frameworks, including evaluation pipelines, harnesses, benchmark datasets, regression tests, and automated quality scoring.
Design retrieval pipelines using enterprise semantic layers, knowledge graphs, vector search, and business ontologies to ground AI agents in enterprise context.
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 planning, reasoning, tool execution, and human collaboration across enterprise workflows.
Operate LLM- and agent-powered applications using LLMOps and AgentOps practices, including prompt versioning, evaluation, experimentation, routing, cost optimization, and lifecycle management.
Develop AI capabilities for enterprise digital twins by integrating operational, commercial, and enterprise data into simulations, predictions, and decision-support workflows.
Collaborate with technical leads, software engineers, data engineers, and AI engineers; contribute to sprint planning, backlog refinement, technical design, code reviews, and problem-solving.
Stay current with AI, machine learning, and generative AI developments; test and validate solutions and contribute to knowledge sharing and engineering best practices.