Description

Lead the design and delivery of scalable AWS Data Lake architectures and end-to-end machine learning systems for petabyte-scale data.

This role is on-site.

Responsibilities

• Architect and manage AWS Data Lake solutions using S3, Glue, Redshift, Athena, and Lake Formation.
• Design and deploy production ML pipelines for real-time inference, batch predictions, and model-as-a-service on AWS and Databricks.
• Lead a team of 6 data scientists and engineers, providing technical mentorship and defining data science operations strategy.
• Implement MLOps practices including CI/CD for ML, automated retraining, drift detection, and monitoring.
• Drive big data processing using Apache Spark and Databricks for structured and unstructured datasets.

Required Skills

• 10+ years of experience in data engineering and machine learning.
• Expert proficiency in AWS services: SageMaker, S3, EMR, Lambda, Step Functions, IAM.
• Deep expertise in Databricks, MLflow, and Unity Catalog for governance.
• Strong command of Python and SQL for data science and pipeline development.
• Experience with ML frameworks: Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Proficiency in Git, branching strategies, and code review practices.
• Knowledge of containerization (Docker) and orchestration (Kubernetes/ECS).

Preferred Skills

• Experience leading distributed teams across onshore and offshore locations.
• Background in establishing responsible AI standards and governance.

Key Skills

Education

Any Graduate