Desired Competencies (Technical/Behavioral Competency)

Must-Have**

(Ideally should not be more than 3-5)

Strong hands-on experience with AWS Data Services:

Amazon S3, Glue, Redshift, Athena, Kinesis, EMR.

Expertise in AWS AI/ML Services:

Amazon SageMaker, Comprehend, Rekognition, Lex, Translate.

Proficiency in Python, SQL, and data engineering frameworks.

Experience with ETL design, data modeling, and data lake architecture.

Familiarity with AWS security best practices (IAM, encryption, compliance).

Strong understanding of MLOps and model deployment on AWS.

Good-to-Have(Ideally should not be more than 3-5)

Knowledge of Generative AI and LLM integration using AWS services.

Experience with AWS Lambda, Step Functions, and serverless architectures.

Exposure to data visualization tools (QuickSight, Tableau).

Familiarity with multi-cloud or hybrid cloud strategies.

Understanding of AI governance, bias detection, and responsible AI practices.

SN

Responsibility of / Expectations from the Role

1

Architect and implement data pipelines and AI/ML solutions using AWS services.

Design and optimize data lakes, warehouses, and analytics platforms on AWS.

Develop and deploy machine learning models using Amazon SageMaker and integrate them into production workflows.

Collaborate with data scientists, engineers, and business teams to deliver end-to-end AI solutions.

Ensure data security, compliance, and governance across AWS environments.

Implement real-time and batch data processing using AWS services like Kinesis, Glue, and Lambda.

Optimize cost, performance, and scalability of data and AI workloads.

Provide technical leadership, best practices, and mentoring to teams.