Data/Machine Learning Engineer II
AI in this role
Job Description Summary:
Our view of digital is one of an integrated ecosystem of platforms that create value across the digital and physical world. Our digital strategy creates value not only for our consumer and customers but across our organization and system. As the Lead Data Engineer, you will be a technical lead for data products and subject matter expert to a team of data engineers. The successful candidate should be an analytical thinking, self-learner, and be able to lead a team through proactive solution development. If you have a passion for innovation, be at the forefront of our iconic digital transformation.
What You’ll Do for Us
- Design and develop scalable web applications using React, TypeScript/JavaScript, HTML, and CSS.
- Build backend services and REST APIs using technologies such as Java/Spring Boot, Python, .NET, or equivalent.
- Design and develop integrations among Salesforce, web and mobile applications, CRM platforms, databases, and enterprise systems.
- Work with relational databases such as SQL Server, including data modeling, queries, and application integration.
- Build secure API-driven solutions using modern authentication and authorization patterns.
- Data & Machine Learning Engineering
- Work with data from enterprise data platforms, customers, and external data providers to develop intelligent product capabilities.
- Develop data preparation, transformation, and feature engineering pipelines.
- Build predictive, classification, ranking, recommendation, segmentation, or forecasting models based on business requirements.
- Evaluate when to use traditional machine learning versus advanced techniques such as deep learning, transformers, computer vision, embeddings, vector search, or RAG.
- Develop batch or online model inference pipelines and services.
- Expose model predictions and recommendations through APIs for consumption by Salesforce, CRM, web, and mobile applications.
- Implement model and data validation, automated testing, and quality controls.
- Production Engineering & MLOps
- Build CI/CD pipelines for application and machine learning workloads.
- Support experiment tracking, model versioning, deployment, and lifecycle management.
- Monitor applications and models for availability, performance, data quality, and model degradation or drift.
- Support production solutions and troubleshoot issues across systems.
- Improve scalability, reliability, security, observability, and maintainability.
- Own capabilities throughout their lifecycle, from requirements through production support and continuous improvement.
Qualification & Requirements
- 5–10 years of professional software engineering experience, with experience spanning application development, data, or machine learning systems.
- Strong programming experience iat least one enterprise application development language such as Pyton, Java, C#, or equivalent.
- Strong experience with JavaScript/TypeScript and modern web development.
- Hands-on experience with React or a comparable modern frontend framework.
- Experience designing and developing REST APIs and backend services.
- Strong SQL skills and experience with relational databases such as SQL Server.
- Experience developing data pipelines, transforming datasets, and performing feature engineering.
- Practical experience developing machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or equivalent.
- Experience taking machine learning solutions beyond experimentation into production inference.
- Experience integrating multiple enterprise systems using APIs and modern integration patterns.
- Experience with automated testing, containers, monitoring, and production support.
- Strong software engineering fundamentals including modular design, testing, code reviews, debugging, and maintainability.
- Ability to work across Product, Data, Architecture, Business, and Engineering teams to translate requirements into production solutions.
- Experience using CI/CD tools – GitHub, GoCD, Terraform and/or Azure DevOps
- Solid knowledge about data modeling and architecture
- Knowledge with developing applications using public cloud, preferably Azure, specially focused in Data Services (CosmosDB, DynamoDB, Databricks, Glue, DataLake, Redshift, Azure Synapse etc.)
- Understand Agile Scrum, CI/CD, DevOps best practices
- Solid understanding of Git-based version control
What We Can Do For You
- Innovation & Technology: The ability to work with an award-winning team that is on the cutting edge of innovation.
- Exposure to World Class Leaders: Availability to global technology leaders that will expand your network and exposure you to emerging technologies and techniques.
- Agile Work Environment: We embrace agile with management that believes in removing barriers and empowering you to experiment, iterate and innovate.
Skills:
Agile, Azure Devops, Business Intelligence Software, DevOps, GitHub, Microsoft Azure, Non Relational Databases, Object-Oriented Programming (OOP), Python (Programming Language), Scala (Programming Language), Software Development, Software Engineering, Structured Query Language (SQL), Terraform (Software), Testing MethodologyPay Range:
United States: 171,000 - 198,000 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:
15Annual 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:
October 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.How we rate this
Data/Machine Learning Engineer II at Coca-Cola rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through a computer vision problem you solved, from raw data to a deployed model.
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, ML Ops, Computer Vision, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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