Description

Design and implement end-to-end machine learning pipelines on AWS to deliver predictive models and actionable business insights.

This role is on-site.

Responsibilities

• Design and implement end-to-end ML pipelines using Amazon SageMaker, AWS Glue, AWS Lambda, and Amazon S3.
• Perform data collection, cleaning, and feature engineering to prepare datasets for modeling.
• Develop predictive models and statistical analyses using Python or R.
• Deploy, monitor, and optimize ML models in production using AWS MLOps best practices.
• Collaborate with data engineers to design ETL pipelines and utilize AWS analytics services (Athena, Redshift, QuickSight, EMR) for reporting.

Required Skills

• 5+ years of experience in data science or machine engineering.
• Strong proficiency in Python and/or R for statistical analysis and modeling.
• Hands-on experience with Amazon SageMaker for ML development and deployment.
• Proficiency in AWS Glue and AWS Lambda for data processing and serverless functions.
• Experience designing and managing ETL pipelines for data availability and reliability.
• Familiarity with AWS S3, Athena, Redshift, QuickSight, and EMR.
• Understanding of MLOps best practices for production model monitoring and optimization.

Preferred Skills

• Experience applying AI/ML algorithms for forecasting, anomaly detection, NLP, computer vision, or recommendation systems.
• Knowledge of security and governance standards for data management on AWS.

Key Skills

Education

Any Graduate