​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:

• 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.

If this position is of interest to you, apply now!