Machine learning engineer (Nezahualcóyotl)

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)