Description

You will own the design, delivery, and operation of production machine learning systems and data infrastructure.

This role is remote.

Responsibilities

• Translate business requirements into problem statements, KPIs, and experiment plans including A/B testing and backtests.
• Design data and ML architectures on lakehouse or warehouse stacks, ensuring scalable ingestion and feature engineering pipelines.
• Build and orchestrate batch and streaming pipelines using Python and SQL, managing model training and experiment tracking.
• Deploy and serve models on major cloud ML platforms with CI/CD, canary releases, and rollback guardrails.
• Monitor model performance and data quality, implementing drift detection and closing the loop with BI dashboards.

Required Skills

• 8+ years in applied ML and data engineering, with 3+ years leading production ML delivery.
• Expert Python proficiency with production-grade SQL and experience with pandas/Polars.
• Strong modeling experience with scikit-learn, XGBoost, or LightGBM.
• Fluency in core ML toolkits including TensorFlow and PyTorch.
• Hands-on experience with data platforms such as Oracle Exadata, Spark/Databricks, or Snowflake/BigQuery/Redshift.
• Proven track record with orchestration tools like Airflow, Prefect, or Dagster.
• Production deployment experience on SageMaker, Vertex AI, or Azure ML.
• Knowledge of MLOps practices including CI/CD, model registries, and observability.

Key Skills

Education

Any Graduate