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# FTC Engineer (Machine Learning)

- **Company:** The Coca-Cola Company
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $178,300.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Unit Testing, Microsoft Azure, Batch Processing, Big Data, Cloud Computing, Code Review, Data Cleansing, Distributed Systems, Fault Tolerance, Monitoring of Systems, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Azure Machine Learning, Secure Coding, Software Construction, Software Engineering, Feature Engineering, System Availability, Delivery Pipeline, Containerization, Kubernetes, Infrastructure Automation Frameworks, Bicep, Machine Learning Operations, Terraform, Software Version Control, Docker, Microservices
- **Published:** September 30, 2026
- **Apply:** https://www.careerjet.com/jobad/usc11408c0b4915a2699a07c08a9900c9d

## About the Role

Strong experience in machine learning development and lifecycle management
Hands-on experience with MLOps practices and production ML systems
Expertise in Python and ML frameworks/libraries
Experience with Azure and/or AWS cloud platforms
Deep understanding of distributed systems and scalable architectures
Proficiency in Docker, Kubernetes, and Infrastructure as Code tools
Demonstrated technical leadership and mentoring experience
Strong written and verbal communication skills
Preferred Qualifications:-
Experience working with Azure ML Studio or similar platforms
Experience in regulated industries (finance, healthcare, etc.)
Familiarity with advanced monitoring and observability tools
Understanding of AI governance, compliance, and security
Contributions to ML/AI communities or open-source projects
Success Profile
Strategic thinker with strong analytical and problem-solving skills
Passion for building scalable, production-ready ML systems
Ability to bridge technical complexity and business value
Strong collaborator with leadership and cross-functional teams
Execution-focused with a commitment to high-quality delivery

## Description

Core ML Expertise
Lead the development of end-to-end machine learning solutions, including:
Feature engineering
Model training, validation, and evaluation
Design and optimize ML models for performance, scalability, and reliability
Work with platforms such as Azure ML Studio or equivalent ML platforms
Ensure best practices in data preparation, model experimentation, and reproducibility
MLOps & Lifecycle Management
Design and implement production-grade ML pipelines supporting:
o Batch processing
o Real-time inference
Manage the full MLOps lifecycle, including:
o Model deployment and scaling
o Model monitoring (data drift, concept drift, performance degradation)
o Model versioning and governance
Automated retraining workflows
Establish robust CI/CD pipelines and workflows for ML systems
Ensure reliability, observability, and continuous improvement of ML solutions
Cloud, Platform & Scalability
Architect and deploy ML systems across cloud platforms (Azure, AWS)
Design scalable distributed systems for large-scale data and model processing
Leverage modern infrastructure tools such as:
o Containerization (Docker)
o Orchestration (Kubernetes)
o Infrastructure as Code (Terraform, ARM/Bicep)
Ensure high availability, fault tolerance, and security in production environments
Software Engineering Excellence
Develop robust, maintainable systems using Python
Design and implement microservices-based architectures
Apply secure coding practices and ensure compliance with data protection standards
Enforce software engineering best practices including:
o Code reviews
o Unit and integration testing
o CI/CD pipelines and automation
Technical Leadership & Influence
Provide technical leadership and define ML architecture standards and best practices
Guide critical design decisions for complex ML systems
Mentor and support senior engineers and cross-functional teams
Translate complex business problems into scalable, secure, and resilient ML solutions
Partner with stakeholders to align ML initiatives with strategic business goals
Communication & Execution Skills
Produce clear and comprehensive technical documentation, including:
o Architecture diagrams
oDesign documents
o Implementation guides
Conduct architecture walkthroughs for technical and non-technical audiences
Communicate effectively with:
o Executive leadership
o Business stakeholders
o External vendors and partners
Drive execution with strong ownership, prioritization, and delivery focus


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