Data AWS Sagemaker

Mumbai - India

Monthly Salary: Not Disclosed

Posted on: 13-10-2025

Vacancies: 1 Vacancy

Job Summary

Technical Skills & ExpertiseSQL: Expert-level proficiency (must have).AWS: Redshift S3 ECS Lambda Glue SQS SNS CloudWatch Step Functions CDK SQS/SNS Athena (must have).PySpark: Expert (must have).Python: Strong experience with API integrations data handling and automation.(must have)LLM Integration: Experience integrating LLMs via APIs (e.g. OpenAI Claude Bedrock) into data workflows or analytics : Understanding of prompt design prompt tuning RAG patterns and model evaluation in Modeling & Query Tuning: Hands-on experience in designing optimized schemas and writing performant Data Ecosystem: Solid understanding of Hadoop Hive Tools: Airflow (Open Source) MWAA on AWS (intermediate- nice to have).Data Migration: Experience with AWS Data Migration Service (DMS).Analytical Skills: Strong in Exploratory Data Analysis (EDA).ETL Design Patterns: Proficiency with window functions reusable ETL frameworks and scalable KnowledgeExposure to Data Lake vs. Data Warehouse in real-time data ingestion and streaming -on with data quality compliance and governance with security best practices in AWS with data enrichment or summarization using with RAG pipelines vector databases (e.g. OpenSearch Pinecone FAISS) and metadata extraction using ResponsibilitiesDesign & Build Intelligent Data Solutions: Develop and maintain scalable LLM-enabled data pipelines using AWS services like Glue Redshift Lambda Step Functions S3 Athena and LLM APIs or fine-tuned models (e.g. OpenAI HuggingFace Amazon Bedrock SageMaker JumpStart) into existing AWS-based data RAG (Retrieval-Augmented Generation) pipelines using AWS services and LLMs for advanced analytics and knowledge Development: Build efficient reusable ETL frameworks that can incorporate LLMs for data enrichment summarization classification or metadata orchestration tools like Airflow/MWAA or Step Functions to manage both data and LLM & AI Services in AWS: Work with Amazon Bedrock SageMaker or custom containers to deploy monitor and scale LLM-based LLM usage for performance and cost within the AWS ecosystem (e.g. caching responses throttling model selection).Data Security & Integrity: Implement security best practices and compliance standards for handling sensitive data within LLM proper prompt auditing logging and governance for LLM & Troubleshooting: Continuously monitor pipelines (including LLM calls) troubleshoot failures and optimize performance and : Maintain detailed documentation of data architectures frameworks and engineering & Reviews: Participate in solution architecture code reviews and sign-offs to ensure quality and & Stakeholder Management: Apply Agile/Scrum methodology for project delivery manage risks and effectively communicate with both technical and non-technical stakeholders.