Senior Director, AI Engineering Lead

The Coca-Cola Company•Atlanta, GA

1d•$202,000 - $229,000

About The Position

As part of the Product & Engineering team within Global Digital Network, the Senior Director, AI Engineering Lead shapes how artificial intelligence is engineered, scaled, governed, and adopted across key digital products. The role combines strategic technology leadership, organizational capability building, and technical expertise to accelerate secure, scalable, business-impacting AI solutions. Reporting to the Head of Product Engineering and partnering with Product, Data Science, Technical Leads, and the Engineering Excellence Lead, this leader defines AI engineering strategy, leads a shared team of AI Engineers, and establishes architectures, platform requirements, and reusable capabilities for enterprise-scale AI. The role ensures AI solutions are secure, scalable, production-ready, and integrated into the product ecosystem, while establishing standards, tooling, and practices that accelerate adoption. The successful candidate is a hands-on technical leader with deep AI engineering expertise and experience building teams and scaling engineering capability. They have designed, built, and operated production AI systems, evolved practices in response to changing AI capabilities, and coached engineers to deliver high-quality solutions. They balance innovation with pragmatism and make considered trade-offs among speed, cost, reliability, safety, and maintainability.

Requirements

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.
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, RAG, tool and function calling, prompt engineering, evaluation methods, and AI interoperability using MCP, A2A, or similar standards.
Hands-on expertise in backend engineering, API and service design, and cloud-native application development.
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 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.
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, with the ability to align engineers, partner with Product and Data Science, and influence senior stakeholders.
Willingness to remain hands-on when needed, tackling complex challenges while coaching others to deliver to a high standard.
Must be currently authorized to work in the United States on a full-time basis and must not require Coca-Cola Company sponsorship to continue working legally in the United States.

Responsibilities

Define the enterprise AI engineering roadmap in partnership with Core Technology and Engineering teams, covering AI platforms, orchestration and memory capabilities, developer tooling, reusable services, and emerging frameworks.
Build, lead, and develop a shared AI Engineering team supporting products across the portfolio; grow organizational capability through hiring, coaching, technical mentorship, and career development.
Provide player-coach technical leadership on complex AI initiatives, advising on model selection, prompt and agent architectures, retrieval and training pipelines, evaluation strategies, and other engineering decisions; selectively implement high-impact capabilities.
Establish enterprise AI reference architectures and reusable patterns for semantic layers, knowledge graphs, context engineering, RAG, GraphRAG, multi-agent systems, agent communication, tool orchestration, memory strategies, and secure interoperability standards such as MCP and A2A.
Develop reusable SDKs, templates, CI/CD patterns, testing frameworks, and engineering accelerators, and partner with Core Technology on reliable, scalable AI platform and orchestration capabilities.
Establish enterprise operating patterns for prompt lifecycle management, evaluation pipelines, observability, experimentation, cost optimization, deployment, and continuous improvement of production AI agents.
Define AI-specific deployment patterns and operational requirements that help product teams ship AI safely at scale.
Establish enterprise telemetry, tracing, runtime monitoring, reasoning diagnostics, token-consumption analytics, operational dashboards, and evaluation frameworks for AI agents and LLM-powered applications.
Improve model quality, latency, token efficiency, runtime cost, observability, and business outcomes through experimentation and engineering optimization.
Partner with governance leads to implement evaluation, guardrails, monitoring, access controls, audit logging, human-in-the-loop workflows, and secure agent execution.
Partner with Product, Data, and Core Technology teams to establish AI architectures and reusable capabilities for enterprise digital twin solutions across commercial and operational domains.
Collaborate 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 support adoption across the product portfolio.
Promote AI engineering best practices, reusable components, tooling, and patterns; help upskill engineers and build AI engineering capability across the organization.