ai engineer for clinical trials

EPAM develops enterprise software products, open source solutions, and accelerators. The role supports AI/GenAI applications for Risk-Based Quality Management in clinical trials.

• Design and build RAG document ingestion pipelines for clinical trial quality data;
• Build and manage vector databases using AWS OpenSearch for RAG-powered AI workflows;
• Develop batch and streaming ETL/ELT pipelines from scratch for unstructured clinical data;
• Build and expose data APIs for AI application consumption;
• Optimize chunking strategies, embedding generation, and retrieval performance for RAG architectures;
• Manage data quality, lineage, and governance for AI/ML data pipelines;
• Deploy and maintain AWS data infrastructure;
• Collaborate with Data Scientists and Backend Developers in an integrated pod team.

• 5+ Years of hands-on data engineering experience at scale;
• Expertise in RAG document ingestion pipelines, including chunking, embedding, and vector indexing;
• Proficiency in AWS OpenSearch as a vector database for RAG workflows;
• Advanced proficiency in Python, including SQL and Spark SQL;
• Experience transforming unstructured data such as PDF and DOCX for RAG/LLM applications;
• Familiarity with AWS Services including S3, Lambda, Glue, Athena, Bedrock, Step Functions, API Gateway, CloudWatch, and DynamoDB;
• Knowledge of containerization with Docker;
• Ability to build custom pipelines from scratch beyond configuring out-of-the-box services;
• English proficiency at B2+ level;
• Nice to have: pharmaceutical or life sciences background, Snowflake, Pinecone, SageMaker processing jobs, CI/CD tools such as Jenkins and Git/Bitbucket, infrastructure tools such as CDK or Terraform, clinical data standards including CDISC, ADaM, and SDTM.

• No conditions specified