- Responsible for the entire ML pipeline, from data ingestion and preprocessing to model training, evaluation, and deployment

End-to-End ML Application Development :

- Design, development, and deployment of machine learning models and intelligent systems into production environments, ensuring they are robust, scalable, and performant.

Software Design & Architecture :

- Apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services.

- Contribute significantly to the architectural decisions for our ML platform and applications.

Data Engineering for ML :

- Design and implement data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference.

MLOps & Productionization :

- Establish and implement best practices for MLOps, including CI/CD for ML, automated testing, model versioning, monitoring
(performance, drift, bias), and alerting systems for production ML models.

Performance & Scalability :

- Identify and resolve performance bottlenecks in ML systems.

- Ensure the scalability and reliability of deployed models under varying load
conditions.

Documentation :

- Create clear and comprehensive documentation for ML models, pipelines, and services.