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Senior Director, AI Operations Governance
Overview
The Senior Director, AI Operations Governance leads the operational engine that governs how The Coca\-Cola Company designs, reviews, approves, deploys, and retires Artificial Intelligence across the enterprise. The role establishes and runs the intake, review, and approval process for AI use cases across every function and Operating Unit worldwide — enabling the business to move at speed while ensuring every AI solution is safe, secure, standardized, and compliant with Company policy and applicable law.
Reporting to the Senior Vice President \& Chief Information Security Officer (CISO), this role serves as the CISO organization's senior operating leader on AI Governance and as a member of the Company's AI Risk Governance Council. Under delegated authority, the Senior Director holds tiered decision rights — approving lower\-risk use cases within defined guardrails, requiring corrections on cases that don't meet standard, and escalating high\-risk, novel, or regulated use cases to the Council or CISO for final decision.
This is a people\-leader role. The Senior Director builds and leads a global team of governance professionals and partners closely with Legal, Privacy, the Data \& AI organization, Cyber Risk, Cyber Defense, Internal Audit, and business leaders across Operating Units — bringing the enterprise one clear, consistent, and credible AI governance experience from idea to production and beyond.
Key Responsibilities
AIOperationalGovernance Program Ownership
- Own the Company's enterprise AI operations governance model — the process, standards, roles, decision rights, and tools that govern AI use cases at Coca\-Cola.
- Ensure the program enables rapid, safe, and operationally aligned AI adoption across every function and Operating Unit worldwide.
- Set the operating cadence — intake reviews, tiered risk boards, exception forums, and escalation paths to the AI Risk Governance Council.
- Continuously improve the program so governance keeps pace with the Company's AI strategy, ambitions and the evolving external landscape.
Use\-Case Intake, Review \& Approval
- Operate a single, well\-known intake process for AI use cases and tools across all functions and Operating Units, from ideation through deployment.
- Classify each use case (including tools and products) by risk tier using clear, published criteria that reflect strategic alignment, business impact, return on investment, data sensitivity, model type, autonomy, and regulatory exposure.
- Under delegated authority, approve, require corrections on, or deny lower\- and medium\-risk use cases within defined guardrails.
- Escalate high\-risk, novel, or regulated use cases with clear recommendations to the AI Risk Governance Council or CISO for final decision.
- Ensure every decision is documented with rationale, conditions, and auditable evidence.
AI Solution Lifecycle Governance
- Embed governance into the full AI solution lifecycle — ideation, design, data sourcing, build, validation, deployment, ongoing monitoring, re\-validation, and retirement.
- Define lifecycle gates and evidence requirements appropriate to each risk tier (e.g., human oversight, bias and fairness testing, security review, privacy review, model documentation, monitoring plan).
- Ensure retired or replaced AI systems are formally decommissioned, with data and access appropriately closed out.
- Oversee operations of model provisioning and approval, agent build approval, agent publishing approval, and approval of models and tools to be used Company\-wide.
Policies, Standards \& Guardrails
- Own the Company's AI operating standards, and operating guardrails, keeping them clear, current, and workable for the business with oversight from the AI Risk Governance Council.
- Coordinate with Legal, Privacy, Cyber Risk Management, Data \& AI, and Ethics \& Compliance so guardrails reflect legal, regulatory, ethical, security, and business realities.
- Communicate requirements and guardrails to the Company through clear guidance, training, and self\-service tooling that helps business teams do the right thing by default.
Cross\-Functional Partnership \& Council Engagement
- Serve as a working member of the AI Risk Governance Council (Chair: CISO; Vice\-Chair: Senior Director Cyber Risk Management), bringing forward operational insight, decision papers, and escalations.
- Partner day\-to\-day with Legal, Privacy, the Data \& AI organization, Ethics \& Compliance, Internal Audit, and Cyber leadership to ensure a single, coherent enterprise governance experience.
- Build durable relationships with Operating Unit \& functional leaders and Bottling System partners so governance is understood, respected, and used.
- Champion consensus\-building across functions with different priorities and operating models.
Regulatory \& Standards Alignment
- Ensure the operating program aligns to leading standards — including NIST AI Risk Management Framework and ISO/IEC 42001 — and to the global body of AI regulation, including the EU AI Act, Chinese AI rules, U.S. state AI laws, and emerging frameworks in other Coca\-Cola markets.
- Work with the Senior Director Cyber Risk Management and Legal counsel to translate regulatory obligations into practical governance requirements.
- Adjust the operating program as regulations evolve, so the Company stays ahead rather than reacting to change.
AI Inventory, Reporting \& Assurance
- Maintain an accurate, enterprise\-wide inventory of AI systems, their risk tiers, owners, decisions, and lifecycle status.
- Produce clear, executive\-framed reporting to the CISO, Cyber Risk Management, the Council, and senior management on AI operational governance posture, throughput, risk trends, and material decisions.
- Develop and maintain a regular cadence with Cyber Risk Management (Senior Director, Cyber Risk) on AI Policy adherence, AI risk and issues trends, data feeds for Cyber GRC risk reporting and metrics for AI, challenges in execution of AI controls, and AI Council escalations.
- Support Internal Audit, external auditors, and regulator engagements with defensible narratives, decision records, and evidence.
- Partner with Cyber Defense on assurance activities that test whether guardrails hold in the real world (e.g., AI red teaming, shadow\-AI discovery).
People Leader
- Build and lead a global AI Operations Governance team, sized to the Company's AI ambitions and risk posture.
- Set a clear vision, hire strong talent, and develop a deep bench of AI operations professionals, both global/corporate and at the operating unit level.
- Hold the team accountable for outcomes — throughput, decision quality, evidence rigor, and business enablement.
- Foster an inclusive, mission\-oriented, high\-performance culture across a networked, global team.
- Manage budget, vendor relationships, and platform investments with discipline.
Qualifications
- Minimum 15\+ years of progressive experience in AI governance, technology risk, data governance, privacy, or regulatory compliance in large, global organizations, with a meaningful portion focused on AI or emerging technology.
- Demonstrated experience building or running a technology governance program with intake, review, and approval authority.
- Strong working command of leading AI governance frameworks (NIST AI RMF, ISO/IEC 42001\) and the global AI regulatory landscape (EU AI Act, Chinese AI rules, U.S. state AI laws).
- Working knowledge of AI technology fundamentals — machine learning, large language models, agentic AI, third\-party AI — enough to engage credibly with technical and business teams.
- Proven ability to operate across a networked, matrixed, global organization and partner ecosystem (Operating Units, bottling system, or analogous complex networks).
- Strong people\-leadership experience, including hiring, developing, and leading multi\-disciplinary global teams.
- Exceptional executive communication and influence skills; able to translate complex governance and regulatory topics into clear choices for senior business and technology leaders.
- Experience partnering closely with Legal, Privacy, Data, Cyber, Ethics \& Compliance, and Internal Audit on cross\-functional governance.
- Relevant certifications such as IAPP AIGP, CIPP/E, CIPM, CDPSE, CISM, or CRISC are strongly preferred.
Education
- Bachelor's degree in Law, Public Policy, Information Systems, Computer Science, Data Science, Risk Management, or related field required.
- Master's degree, JD, MBA, or advanced credential (AIGP, CIPP/E, CISSP, CISM, or equivalent) highly desirable.
The Coca\-Cola Company will not offer sponsorship for employment status (including, but not limited to, H1\-B visa status and other employment\-based nonimmigrant visas) for this position. Accordingly, all applicants must be currently authorized to work in the United States on a full\-time basis and must not require The Coca\-Cola Company's sponsorship to continue to work legally in the United States.Pay Range:
United States of America: 202,000 USD \- 229,000 USD*Base 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:
30*Annual 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:
NoJob Posting End Date:
August 14, 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.
Pay Range:United States of America: 0 USD \- 0 USD
Base 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:30
Annual 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.
Long\-term Incentive Reference Value Percentage:0 \- 20
Long\-term Incentive reference value is a market\-based competitive value for your role.
Salary Context
This $202K-$229K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Coca-Cola Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $202K to $229K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The Coca-Cola Company AI Hiring
The Coca-Cola Company has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $115K - $247K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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