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