GenAI Data Engineer

Your responsibilities: • Design and maintain scalable data pipelines using PySpark, Python, and distributed computing frameworks to support high‑volume data processing. • Architect and optimize AWS-based data and AI infrastructure, ensuring secure, performant, and cost‑efficient ingestion, transformation, and storage. • Develop, finetune, benchmark, and evaluate GenAI/LLM models, including custom training and inference optimization. • Implement and maintain RAG pipelines, vector databases, and document-processing workflows for enterprise GenAI applications. • Build reusable frameworks for prompt management, evaluation, and GenAI operations. • Collaborate with cross-functional teams to integrate GenAI capabilities into production systems and ensure high-quality data, governance, and operational reliability Your Profile Essential skills/knowledge/experience: • Strong experience with PySpark, distributed data processing, and largescale ETL/ELT pipelines. • Strong SQL expertise including star/snowflake schema design, indexing strategies, writing optimized queries, and implementing CDC, SCD Type 1/2/3 patterns for reliable data warehousing. • Advanced proficiency in Python for data engineering, automation, and ML/GenAI integration. • Hands‑on expertise with AWS services (S3, Glue, Lambda, EMR, Bedrock / custom model hosting). • Practical experience with GenAI/LLM model creation, finetuning, benchmarking, and evaluation. • Solid understanding of RAG architectures, embeddings, vector stores, and LLM evaluation methods. • Experience working with structured and unstructured datasets (documents, logs, text, images). • Familiarity with scalable data storage solutions (Delta Lake, Parquet, Redshift, DynamoDB). • Understanding model optimization techniques (quantization, distillation, inference optimization). • Strong capability to debug, tune, and optimize distributed systems and AI pipelines. Desirable skills/knowledge/experience: (As applicable) • Pyspark, Python, SQL,AWS, GenAI