Senior Director, AI Engineering Lead
Job Description Summary:
Role Overview
As part of theProduct & Engineering team withinGlobal Digital Network, the Senior Director,AIEngineering Lead will play a pivotal role in shaping how artificial intelligence is engineered, scaled, governed, and adopted across ourkeydigitalproductsportfolio. This role combines strategic technology leadership, organizational capability building, and deep technicalexpertiseto accelerate the delivery of secure, scalable, and business-impacting artificial intelligence solutions.
Reporting to the Head ofProductEngineering and partnering closely with Product, Data Science, Technical Leads, and the Engineering Excellence Lead, you will define the AI engineering strategy, lead a team of AI Engineers, and establish the architectures, platform requirements, and reusable capabilities that enable AI at enterprise scale. You will ensure AI solutions are secure, scalable, production-ready, and seamlessly integrated into our product ecosystem, while shaping engineering standards, tooling, and best practices that accelerate AI adoption across the organization.
The ideal candidate is a hands-on technical leader who combines deep AI engineeringexpertisewith the ability to build teams and scale engineering capability. You have successfully designed, built, andoperatedproductionAI systems, evolved engineering practices based on rapidly changing AI capabilities, and coached engineers to deliver high-quality AI solutions. You balance innovation with pragmatism, making thoughtful trade-offs between speed, cost, reliability, safety, and maintainability.
WhatYou’llDo 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: operateas 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: establishenterprise patterns for prompt lifecycle management,evaluationpipelines, 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 toestablishAI 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, witha track recordof shipping production systems that serve real users at scale
Demonstrated experience designing, deploying, andoperatingproduction 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-onexpertisein 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 asLangGraph,LangChain, Semantic Kernel,CrewAI, or equivalent, and with model providers such as OpenAI, Anthropic, or Azure OpenAI
Experience withMLOps,LLMOps, orAgentOpspractices, 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 toestablishAI 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
Skills:
Agile Methodology, Application Development, Budgeting, Business Processes, Business Value Creation, Change Management, Decision Making, Financial Forecasting, Leadership, Long Term Planning, Microsoft Azure, Microsoft Office, Negotiation, Process Improvements, Risk Assessments, Risk Management, Software Development, Software Development Life Cycle (SDLC), Strategic Alignment, Strategic IT, Structured Query Language (SQL), Vendor Management, Waterfall ModelPay Range:
United States: 202,000 - 229,000 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
30Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
YesJob Posting End Date:
September 27, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors - curious, empowered, inclusive and agile - and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.