Senior Director, AI Engineering Lead

The Coca-Cola Company; Atlanta (GA); On-site; USD 240,000 - 420,000; Full time

Job summary

The Coca-Cola Company is seeking a Senior Director, AI Engineering Lead to shape how AI is engineered, scaled, governed, and adopted across our digital products portfolio. This role blends strategic technology leadership with hands-on engineering, driving secure, scalable AI solutions for broad business impact.

You will define enterprise AI roadmaps, build and mentor a high-caliber AI engineering team, and partner across Product, Data Science, and Core Technology to deliver reusable AI

Qualifications

• 8–10+ years in software or AI/ML engineering, including 3+ years leading or managing engineering teams, with a track record of shipping production systems that serve real users at scale.
• Demonstrated experience designing, deploying, and operating production AI systems, including end-to-end ML pipelines, modern LLM-powered applications, and agentic AI solutions.
• Strong understanding of LLM-based systems, including agent architectures, Retrieval-Augmented Generation (RAG), tool and function calling, prompt engineering, evaluation methods, and AI interoperability using MCP, A2A, or similar standards.
• Hands-on backend engineering, API and service design, and cloud-native application development, with strong programming skills in Python and one or more of Java, Go, or C++.

Responsibilities

• Define enterprise AI engineering roadmap: partner with Core Technology and Engineering teams to shape AI platforms, tooling, and enterprise adoption.
• Build and lead the AI Engineering team across the product portfolio through hiring, mentorship, and career development.
• Guide complex AI initiatives: model selection, prompt architectures, retrieval/training pipelines, evaluation strategies, and high-impact capabilities.
• Define enterprise AI architecture and interoperability patterns: establish reference architectures for semantic layers, knowledge graphs, RAG, GraphRAG, multi-agent systems, and secure interoperability.
• Advance reusable AI engineering capabilities: develop SDKs, templates, CI/CD patterns, testing frameworks, and accelerators for scalable deployments.
• Establish AI observability and governance: telemetry, runtime monitoring, evaluation frameworks, token analytics, and guardrails.
• Embed responsible and governed AI: implement evaluation, audit logging, access controls, and human-in-the-loop workflows.
• Drive digital twin capabilities: partner with Product/Data/Core Technology to enable AI-powered twins across domains.
• Partner across product and engineering: align business goals to AI capabilities and ensure reusable patterns are adopted.
• Champion AI engineering excellence: promote best practices, upskilling, and reusable components.

Skills

Python

Leadership

LLM systems

Backend engineering

Communication

Education

BS/MS in CS

Tools

LangGraph

LangChain

Semantic Kernel

CrewAI

OpenAI

Azure OpenAI

Job description

Role Overview

As part of the Product & Engineering team within Global Digital Network, the Senior Director, AI Engineering Lead will play a pivotal role in shaping how artificial intelligence is engineered, scaled, governed, and adopted across our key digital products portfolio. This role combines strategic technology leadership, organizational capability building, and deep technical expertise to accelerate the delivery of secure, scalable, and business‑impacting artificial intelligence solutions.

What You’ll Do For Us

• Define the enterprise AI engineering roadmap: partner with Core Technology and Engineering teams to shape the evolution of AI platforms, orchestration and memory capabilities, developer tooling, reusable engineering services, and emerging AI frameworks that accelerate enterprise AI adoption
• Build and lead the AI Engineering team: build, lead, and develop a shared team of AI Engineers supporting products across the portfolio. Grow the organization’s AI engineering capability through hiring, coaching, technical mentorship, and career development. Foster a culture of engineering excellence, experimentation, and continuous learning
• Lead the organization’s most complex AI engineering challenges: operate as a player-coach by providing technical leadership on the organization’s most complex AI initiatives. Partner with Tech Leads and engineering teams on model selection, prompt and agent architectures, retrieval and training pipelines, evaluation strategies, and other critical AI engineering decisions. Selectively contribute to the implementation of high-impact AI capabilities
• Define enterprise AI architecture and interoperability patterns: establish reference architectures and reusable engineering patterns for semantic layers, knowledge graphs, context engineering, Retrieval-Augmented Generation (RAG), GraphRAG, multi-agent systems, agent communication, tool orchestration, memory strategies, and secure interoperability using Model Context Protocol (MCP), Agent-to-Agent (A2A), and emerging enterprise integration standards
• Advance reusable AI engineering capabilities: develop reusable SDKs, templates, CI/CD patterns, testing frameworks, and engineering accelerators that enable product teams to build AI solutions consistently. Partner with Core Technology to ensure the underlying AI platform and orchestration capabilities support reliable and scalable enterprise deployment
• Define AI engineering operating patterns: establish enterprise patterns for prompt lifecycle management, evaluation pipelines, observability, experimentation, cost optimization, deployment, and continuous improvement of AI agents in production. Define AI-specific deployment patterns and operational requirements that enable product teams to ship AI safely at scale
• Establish AI observability and operational excellence: define enterprise-wide telemetry, tracing, runtime monitoring, reasoning diagnostics, token consumption analytics, operational dashboards, and evaluation frameworks for AI agents and LLM-powered applications. Continuously improve model quality, latency, token efficiency, runtime cost, observability, and business outcomes through experimentation and engineering optimization
• Embed responsible and governed AI: partner with governance leads to implement evaluation, guardrails, monitoring, access controls, audit logging, human-in-the-loop workflows, and secure agent execution so AI products are safe, explainable, compliant, and trusted by business users
• Advance digital twin capabilities: partner with Product, Data, and Core Technology teams to establish AI architectures and reusable capabilities that enable enterprise digital twin solutions across commercial and operational domains
• Partner across product and engineering: work shoulder to shoulder with Product Managers, Product Owners, Tech Leads, the Engineering Excellence Lead, Data Science, Design, and Core Technology to translate business goals into AI capabilities, align technical direction, and ensure reusable AI capabilities are successfully adopted across the product portfolio
• Champion AI engineering excellence: promote best practices, reusable components, tooling, and engineering patterns that accelerate AI delivery across squads. Help upskill engineers and continuously build AI engineering capability across the organization

Requirements & Qualifications

• BS or MS in Computer Science, Machine Learning, Engineering, or a related technical discipline, or equivalent practical experience
• 8-10+ years of experience in software or AI/ML engineering, including 3+ years leading or managing engineering teams, with a track record of shipping production systems that serve real users at scale
• Demonstrated experience designing, deploying, and operating production AI systems, including end-to-end ML pipelines, modern LLM-powered applications, and agentic AI solutions
• Strong understanding of LLM-based systems, including agent architectures, Retrieval-Augmented Generation (RAG), tool and function calling, prompt engineering, evaluation methods, and AI interoperability using Model Context Protocol (MCP), Agent-to-Agent (A2A), or similar standards
• Hands‑on expertise in backend engineering, API and service design, and cloud‑native application development, with strong programming skills in Python and one or more of Java, Go, or C++
• Hands‑on experience with agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or equivalent, and with model providers such as OpenAI, Anthropic, or Azure OpenAI
• Experience with MLOps, LLMOps, or AgentOps practices, including prompt lifecycle management, evaluation, experimentation, deployment, monitoring, and production operations for AI systems
• Familiarity with enterprise AI governance practices, including model and prompt approvals, audit logging, data classification, risk controls, and responsible AI
• Experience building AI capabilities for global, enterprise‑scale products used across multiple markets
• Experience supporting enterprise digital twin capabilities or AI‑enabled simulation environments
• Proven ability to establish AI engineering patterns, conduct high-quality code reviews, and develop engineers through coaching, technical mentorship, and hiring
• Excellent communication and storytelling skills, able to align engineers, partner with Product and Data Science, and influence senior stakeholders
• Demonstrated willingness to remain hands‑on when it matters most, rolling up your sleeves to tackle complex challenges while coaching others to deliver at a high standard