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)