Capgemini is looking to unlock the value of technology and build a more sustainable, more inclusive world for the world's leading organizations
Requirements
- Amazon SageMaker
- AWS Glue
- AWS Lambda
- Amazon S3
- Python
- R
- AWS ML Ops
- AWS analytics services (Athena, Redshift, QuickSight, EMR)
Responsibilities
- Design and implement end-to-end machine learning (ML) pipelines using services such as 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, R, or similar tools.
- Deploy, monitor, and optimize ML models in production environments using AWS ML Ops best practices.
- Collaborate with data engineers to design ETL pipelines and ensure data availability and reliability.
- Utilize AWS analytics services (Athena, Redshift, QuickSight, EMR) for advanced reporting and visualization.
- Apply AI/ML algorithms for use cases such as forecasting, anomaly detection, NLP, computer vision, and recommendation systems.
Other
- Bachelor's degree or equivalent experience
- Travel may be required
- Must be eligible to work in the United States
- Paid time off and paid holidays
- Healthcare including dental, vision, mental health, and well-being programs
