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About This Role
At The Coca‑Cola Company, we are committed to building a future fueled by innovation, data, and human ingenuity. As a global leader with a powerful portfolio of brands and an evolving digital ecosystem, we continually invest in technologies that unlock growth, accelerate decision\-making, and enhance the experiences of our consumers and employees. Our Digital Strategy function plays a critical role in shaping this transformation by identifying, scaling, and embedding next\-generation capabilities across the enterprise.
We are seeking a Senior Manager, Artificial Intelligence Workflow Engineering to join our Digital Strategy team, reporting to the Vice President of Digital Strategy. This role sits at the intersection of business process transformation and advanced technology, with a focus on designing, building, and optimizing intelligent workflows powered by artificial intelligence. The ideal candidate is a technically fluent, business\-minded professional who can translate complex problems into scalable, automated solutions that improve efficiency, agility, and value creation across the organization.
The ideal candidate combines strong technical expertise with business acumen, demonstrating the ability to translate complex challenges into scalable, efficient solutions. This individual will partner closely with cross\-functional teams to identify high\-value opportunities for automation and artificial intelligence adoption, while ensuring alignment with enterprise priorities and responsible technology practices. This role offers a unique opportunity to influence how work gets done across Coca‑Cola by shaping and scaling next\-generation digital capabilities that support growth, agility, and operational excellence.
What You’ll Do for Us
- Design and implement artificial intelligence\-enabled workflows that streamline business processes, improve productivity, and enhance decision\-making across enterprise functions
- Partner with the Digital Strategy, Technology, and business teams to identify high\-value use cases for artificial intelligence and workflow automation
- Develop AI solutions or leverage commercially available off\-the\-shelf options for embedding AI across prioritized core workflows selected for AI application
- Collaborate with the AI Network and Community to tap in to key AI skillets across the Digital Org
- Translate business requirements into scalable technical solutions, leveraging modern workflow orchestration tools and artificial intelligence platforms
- Develop and optimize end\-to\-end process automation strategies, ensuring seamless integration across systems and data sources
- Drive continuous improvement by analyzing workflow performance, identifying bottlenecks, and implementing enhancements to increase efficiency and effectiveness
- Collaborate with cross\-functional stakeholders to ensure alignment on priorities, timelines, and expected outcomes for automation initiatives
- Establish best practices and governance frameworks for artificial intelligence–driven workflows, ensuring ethical, responsible, and compliant use of technology
- Partner with Change Management and Capabilities teams to drive training, upskilling, communications, and adoption of intelligent workflows across impacted teams
- Support the development of scalable frameworks and reusable assets that accelerate the deployment of new workflows across regions and functions
- Maintain appropriate information security and governance for all AI solutions
Requirements \& Qualifications
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related field
- 7\+ years of experience in workflow engineering, process automation, digital transformation, or related technology roles
- Proven experience designing and implementing workflow automation solutions using modern platforms and tools
- Strong understanding of artificial intelligence and machine learning concepts, including practical application in business processes
- Demonstrated ability to translate business needs into technical solutions that deliver measurable outcomes
- Experience working across cross\-functional teams to drive alignment and execute complex initiatives
- Proficiency in data analysis and data\-driven decision\-making, with the ability to interpret and leverage insights to optimize workflows
- Familiarity with enterprise systems integration, application programming interfaces, and cloud\-based architectures
- Strong communication skills, with the ability to clearly articulate technical concepts to non\-technical stakeholders
- Knowledge of responsible artificial intelligence practices, including governance, privacy, and ethical considerations
- Experience supporting change management efforts and driving adoption of new technologies across organizations
- Ability to operate effectively in a fast\-paced, evolving environment with a growth mindset and continuous learning orientation
Candidate Attributes
- Demonstrates curiosity and a growth mindset, actively exploring new technologies and continuously building skills in artificial intelligence and automation
- Brings a problem\-solving orientation, approaching ambiguous challenges with structured thinking and a focus on practical outcomes
- Balances strategic thinking with execution, able to move from concept to delivery while maintaining focus on impact
- Collaborates effectively across diverse teams, building strong relationships and aligning stakeholders toward shared goals
- Operates with accountability and ownership, taking responsibility for delivering high\-quality solutions in a dynamic environment
- Shows adaptability and resilience, thriving in evolving priorities and rapidly changing digital landscapes
- Communicates with clarity and influence, tailoring messages to different audiences and driving alignment
- Demonstrates customer and user focus, designing workflows that enhance experience and usability for end users
- Upholds high standards of integrity and ensures responsible, ethical use of data and artificial intelligence
- Brings a continuous improvement mindset, proactively identifying opportunities to enhance processes and outcomes
Core Capabilities Needed
1\. Advanced Workflow Engineering \& Automation Design
Ability to design, architect, and deliver end‑to‑end automated workflows using modern orchestration platforms, integrating multiple systems and data sources to streamline business processes.
2\. Applied Artificial Intelligence \& Machine Learning Fluency
Demonstrates strong understanding of artificial intelligence concepts and practical application within enterprise workflows, including decision automation, predictive models, and intelligent process routing.
3\. Business Process Transformation
Capability to analyze, redesign, and optimize complex business processes by identifying inefficiencies and embedding automation and AI to drive measurable performance improvements.
4\. Systems Integration \& Architecture Thinking
Ability to design solutions that connect enterprise platforms (via APIs, middleware, cloud services), ensuring scalability, reliability, and interoperability across the technology landscape.
5\. Data Literacy \& Insight Application
Interprets workflow performance data, identifies trends and bottlenecks, and applies insights to continuously improve efficiency, quality, and decision\-making outcomes.
6\. Problem Solving in Ambiguous Environments
Applies structured thinking to solve complex, undefined problems; translates evolving business needs into clear technical solutions with practical implementation paths.
7\. Stakeholder Influence \& Cross\-Functional Collaboration
Builds alignment across Digital, Technology, and business teams; influences decisions without direct authority; effectively bridges technical and non\-technical stakeholders.
8\. Digital Strategy Execution
Contributes to broader digital strategy by identifying high\-value use cases, prioritizing initiatives, and executing solutions that align with enterprise transformation goals.
9\. Responsible AI \& Governance Awareness
Ensures solutions adhere to ethical AI principles, including data privacy, bias mitigation, compliance standards, and governance frameworks within workflow design.
10\. Change Enablement \& Adoption Leadership
Drives adoption of new technologies by supporting change management, building user trust, and ensuring solutions are intuitive, scalable, and sustainable in real business environments.
What We’ll Do for You
- Provide the opportunity to shape enterprise\-wide digital transformation initiatives and influence how artificial intelligence is embedded into everyday work
- Offer exposure to global teams, diverse business challenges, and high\-impact projects that accelerate your professional growth
- Support your development through access to cutting\-edge tools, learning resources, and collaboration with leading digital and technology experts
- Create an environment that values innovation, experimentation, and continuous improvement, empowering you to bring new ideas to life
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.
Budget Management, Communication, Data Analytics, DOT Regulations, Group Problem Solving, JDA (Inactive), Microsoft Office, Microsoft Power Business Intelligence (BI), Oracle Transportation Management, SAP Manufacturing Execution (SAP ME), Supply Chain, Tableau (Software), Transportation Logistics, Transportation Management Systems (TMS), Transportation PlanningPay Range:
United States of America: 152,000 USD \- 178,300 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:
15*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:15
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 $152K-$178K range is below 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 Required
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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $152K to $178K.
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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