Machine learning performance engineers

Must Have: Python‐based ML development (production ML pipelines & models)
ML frameworks: Scikit‐learn, XGBoost/Light GBM/Cat Boost, and Py Torch or Tensor Flow
Feature engineering & feature pipelines (large‐scale, automated where applicable)
Big data processing: Apache Spark / Py Spark, Databricks, Delta Lake
MLOps: MLflow (experiment tracking, model registry), model versioning, retraining pipelines
Deployment & Ops: Docker, Kubernetes, REST APIs (Fast API/Flask), CI/CD and cloud ML services (AWS/Azure/GCP)