RAG AI Engineer (Spark & AWS) — OpenSearch Pipelines

EPAM Systems, Inc.; Turkey; On-site; TRY 300,000 - 540,000; Full time

Benefits offered by this job

Continuous upskilling

Medical insurance

English language courses

Well-being events

Job summary

EPAM Systems, Inc. is seeking a Senior AI Engineer to design and maintain data pipelines powering AI/GenAI in Risk-Based Quality Management for clinical trials.

You will own RAG ingestion, vector indexing, and data APIs, building robust ETL/ELT flows and containerized pipelines using AWS services and Spark-based analytics. The role emphasizes collaboration with data scientists and backend developers, ensuring data quality, governance, and scalable AI workflows within a production pod.

Qualifications

• Minimum 5 years of hands-on data engineering experience at scale.
• Expertise in RAG document ingestion pipelines (chunking, embedding, vector indexing).
• Proficiency in Python and Spark SQL; strong SQL skills.

Responsibilities

• Design and build RAG document ingestion pipelines (chunking, embedding, vector indexing) for clinical trial quality data.
• Build and manage vector databases (AWS OpenSearch) for RAG-powered AI workflows.
• Develop batch and streaming ETL/ELT pipelines from scratch for unstructured clinical data (PDF, DOCX).
• Build and expose data APIs for AI applications consumption.
• Optimize chunking strategies, embedding generation, and retrieval for RAG architectures.
• Manage data quality, lineage, and governance for AI/ML data pipelines.
• Deploy and maintain AWS data infrastructure (S3, Lambda, Glue, Athena, Step Functions, DynamoDB).
• Collaborate with Data Scientists and Backend Developers in an integrated pod team.

Skills

RAG pipelines

Python

Spark SQL

SQL

Docker

APIs

Cloud AWS

Tools

AWS OpenSearch

S3

Lambda

Glue

Athena

Step Functions

DynamoDB

Bedrock

API Gateway

CloudWatch

Job description

EPAM Systems, Inc. is seeking a Senior AI Engineer to design and maintain data pipelines powering AI/GenAI in Risk-Based Quality Management for clinical trials.

You will own RAG ingestion, vector indexing, and data APIs, building robust ETL/ELT flows and containerized pipelines using AWS services and Spark-based analytics. The role emphasizes collaboration with data scientists and backend developers, ensuring data quality, governance, and scalable AI workflows within a production pod.