About

• Data Engineering: Build and maintain data pipelines for structured and unstructured data. Work with SQL/NoSQL databases and data warehouses (e.g., Snowflake, BigQuery, Redshift). Ensure data quality, integrity, and availability. Data Science: Explore, clean, and analyze datasets to derive insights. Train, evaluate, and fine-tune machine learning models. Assist in developing proof-of-concepts and production-ready ML solutions. MLOps: Support deployment of ML models into production (batch and real-time). Implement model monitoring, retraining, and CI/CD workflows. Work with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes). Experience with SQL and familiarity with database design. Hands-on experience with deep learning frameworks (TensorFlow, PyTorch). Experience with data visualization tools (Tableau, Power BI, or Looker). Knowledge of distributed computing frameworks (Spark, Dask). Prior internship or project work in MLOps.