AWS Data Scientist

Key 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 Quick Sight EMR for advanced reporting and visualization
• Work with stakeholders to translate business objectives into data science solutions and actionable insights
• Apply AIML algorithms for use cases such as forecasting anomaly detection NLP computer vision and recommendation systems
• Maintain compliance with security and governance standards for data management on AWS

 

Do not share it to the candidates. But, evaluate the candidates on below parameters.

1. End to end ML pipelines, including feature engineering, evaluation, and monitoring strategies
2. Understanding RAG pipelines & architecture key stages like chunking, embeddings, retrieval, and hallucination handling
3. LangGraph - articulate its features or differentiation from LangChain
4. exposure to agentic AI concepts and frameworks
5. Good exposure in AWS stack for deploying AI/ML services.

 

AWS Data Scientist 1Machine Learning,Python,AWS,lambda,AI,Amazon SageMaker,LLM,RAG,Agentic AIN/AFull TImeUnited States