Responsibilities:
- Design and build scalable data pipelines using AWS services such as Glue, Redshift, S3, and EMR to process and transform large datasets.
- Implement best practices for data storage, data lakes, and data warehousing on AWS to ensure optimal performance, security, and cost-efficiency.
- Develop and integrate Generative AI models using AWS SageMaker, Bedrock, and other AI/ML services to deliver intelligent automation and insights.
Build, train, and fine-tune machine learning models, including Generative AI models, for various business use cases. - Implement data governance frameworks to ensure data integrity, quality, and compliance with industry regulations.
- Secure data environments by enforcing best practices for AWS Identity and Access Management (IAM), encryption, and data privacy policies.
- Automate data processing and machine learning workflows using AWS Lambda, Step Functions, and Infrastructure-as-Code (IaC) tools like CloudFormation or Terraform.
- Continuously optimize data architectures and AI solutions to reduce costs and improve performance.
- Collaborate with cross-functional teams to identify business challenges and design data-driven AI solutions that address them.
- Provide technical guidance to teams on AWS data services and AI/ML best practices.
- Monitor and maintain the performance of data pipelines, AI models, and AWS infrastructure, ensuring they are robust, reliable, and scalable.
- Troubleshoot data and AI model issues, providing timely resolutions to maintain uninterrupted operations.
Your skillsets/requirement:
- Bachelor's degree in Computer Science, Data Science, Engineering, or related fields.
- 3-5 years of hands-on experience in AWS data engineering and AI/ML projects.
- Proven experience with AWS services such as Glue, Redshift, S3, SageMaker, and Lambda
