Principal Software Engineer – Data Analytics

Title: Principal Software Engineer – Data Analytics Location: Atlanta, Georgia

The Challenge
Are you passionate about pushing the boundaries of what’s possible with artificial intelligence? Join our innovative AI Core team as a Machine Learning Engineer and help drive the AI transformation across our organization. Our mission is to empower every business unit to leverage AI by building reusable, scalable AI features and infrastructure. As part of our cross-functional team, you’ll work alongside talented engineers and data scientists to develop, deploy, and operationalize cutting-edge AI solutions that make a real impact company-wide.

Your Mission
As a Machine Learning Engineer, you will play a pivotal role in researching, developing, and implementing state-of-the-art AI models and workflows in Python. You will focus on creating production-ready, reusable components and robust pipelines that can be leveraged across multiple business domains. Your work will be at the heart of the company’s AI platform, enabling teams to seamlessly adopt and scale AI capabilities for a wide range of use cases.

Your Responsibilities
Development:

• Design, build, and optimize machine learning models, pipelines, and APIs using Python and state-of-the-art AI frameworks.

• Develop reusable, production-grade components that facilitate rapid AI solution deployment across business units.

• Implement configuration-driven workflows (e.g., YAML) to support scalable and flexible AI system orchestration.

• Ensure high-quality code through comprehensive unit, integration, and exploratory testing (‚â•80% coverage or as per team standards).

Collaboration:
• Partner with backend, frontend, and product teams to integrate AI models into scalable, user-facing features.
• Participate in technical planning, architecture reviews, and sprint ceremonies to align AI development with platform and business goals.
• Share knowledge and collaborate with team members to foster a culture of continuous learning and innovation.

Support:
• Monitor, evaluate, and improve AI model performance in production environments.
• Troubleshoot and resolve issues related to data pipelines, model inference, and deployment.
• Document workflows, best practices, and model configurations to ensure reproducibility and transparency.

Code Review:
• Conduct peer reviews and provide constructive feedback on machine learning code and processes.
• Advocate for best practices in ML engineering, testing, and automation.

Continuous Learning:
• Stay current with advancements in AI/ML, actively exploring and experimenting with new techniques, architectures, and frameworks.
• Bring a spirit of curiosity, experimentation, and passion for AI-even if you have not been able to apply it fully in previous roles.

You Are

An enthusiastic and innovative engineer with a strong desire to solve complex problems using artificial intelligence. You have a solid foundation in Python programming and a deep interest in state-of-the-art AI, whether or not your current role has allowed you to fully pursue it. You thrive in a collaborative, cross-functional environment and are excited to contribute to an ambitious mission of company-wide AI adoption.

Your Experience Includes  

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• 10+ years of professional software engineering experience, with a strong focus on frontend development.

• Hands-on experience with one or more modern AI/ML frameworks (such as TensorFlow, PyTorch, Hugging Face, scikit-learn, or similar).

• Experience designing and deploying production-grade machine learning models and pipelines.

• Familiarity with configuration-driven workflows and orchestration using tools like YAML.

• Experience with cloud-based ML platforms (Azure, GCP, AWS) and containerization (Docker) is a plus.

• Strong testing, debugging, and documentation skills.

• Excellent collaboration and communication abilities.

• Demonstrated curiosity and passion for AI, with a willingness to continuously learn and grow in the field.