Must Have: Python‑based ML development (production ML pipelines & models) ML frameworks: Scikit‑learn, XGBoost/LightGBM/CatBoost, and PyTorch or TensorFlow Feature engineering & feature pipelines (large‑scale, automated where applicable) Big data processing: Apache Spark / PySpark, Databricks, Delta Lake MLOps: MLflow (experiment tracking, model registry), model versioning, retraining pipelines Deployment & Ops: Docker, Kubernetes, REST APIs (FastAPI/Flask), CI/CD and cloud ML services (AWS/Azure/GCP)