Responsibilities: 

• End-to-End ML Development

- Design, train, and evaluate machine learning and deep learning models.
- Translate business problems into data-driven solutions.

•   MLOps & Deployment

- Build scalable pipelines for data preprocessing, feature engineering, and model deployment.
- Monitor and maintain production ML systems, ensuring reliability and performance.

•  Product Ownership

- Take ownership of ML products, from ideation to production rollout.
- Collaborate with product managers, engineers, and business stakeholders to deliver impactful solutions.

• Data Management

- Work with structured and unstructured data, ensuring data quality and integrity.
- Collaborate with data engineering teams to optimize ETL processes.

Requirements:

• Advanced proficiency in Python, SQL, and ML libraries (TensorFlow, PyTorch)
• Advanced capability in designing and deploying ML pipelines with MLOps tools (Airflow, MLflow, Docker, FastAPI).
• Strong knowledge of data preprocessing, feature engineering, and model evaluation.
• Experience with large-scale data (Spark, Hadoop, distributed training).
• Knowledge of software engineering best practices: CI/CD, testing, version control (Git).