Responsibilities
- Build, deploy, and refine machine learning models for practical business applications and customer-facing systems.
- Collaborate with data scientists to transition predictive models into production with scalability, maintainability, and performance in mind.
- Create and manage data pipelines that support model training, inference, and full lifecycle operations.
- Process and manage large, complex datasets to ensure data integrity, reproducibility, and consistent versioning across ML workflows.
- Establish monitoring, logging, and alerting systems to evaluate model performance, identify data or concept drift, and guide retraining efforts.
- Use cloud platforms such as AWS, Azure, and GCP to develop scalable machine learning solutions through managed services and infrastructure-as-code methods.
- Produce clean, modular, and well-documented code following MLOps and software engineering standards.
- Keep updated on advancements in machine learning tools, frameworks, and industry practices to improve platform capabilities over time.