ML Code & Repository Management

• Refactor and optimize Data Science and Machine Learning codebases using software engineering best practices, including modular design, documentation, testing, code reviews, and reusable components.
• Maintain ML code repositories in GitLab, ensuring effective version control, release management, and reproducibility of experiments through proper management of code, datasets, configurations, and model artifacts.

ML Pipeline Design & Automation

• Design, build, and maintain scalable AI/ML pipelines using AWS AI/ML services and enterprise-approved tooling.
• Automate key ML lifecycle stages including data ingestion, preprocessing, feature engineering, model training, validation, model serving, model registry, and retraining workflows.
• Integrate ML workflows with orchestration frameworks such as Apache Airflow and implement automated testing to ensure reliability and operational efficiency.
• Partner with data scientists to productionize models and establish scalable, repeatable deployment patterns.

Production Monitoring & Maintenance

• Own BAU operations of production AI/ML solutions, ensuring reliability, availability, and performance.
• Monitor model health, performance, and operational metrics using AWS monitoring tools and dashboards; investigate and remediate model drift, data drift, and other production issues.
• Coordinate model refresh and retraining activities and communicate model performance, operational metrics, and key insights to stakeholders on a defined cadence.

Collaboration & Governance

• Partner with data scientists, data engineers, UX designers, IT teams, and business stakeholders to operationalize AI/ML solutions aligned with business objectives.
• Maintain technical documentation, including system design, architecture, model lifecycle, and operational procedures.
• Ensure compliance with Delta IT data governance, privacy, security, and regulatory requirements while promoting ML engineering best practices across the development lifecycle.

UI Development & Maintenance

• Develop and support lightweight web applications and user interfaces that enable business users to access AI/ML products and insights through self-service experiences.
  • Integrate AI/ML models, APIs, and data services into user-facing applications while ensuring usability, reliability, and maintainability.