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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain high-performance ETL/ELT data pipelines on AWS using PySpark, Python, SQL across EMR, Glue, Lambda, Step Functions, Redshift, and S3.
Develop, fine-tune, and deploy large language models (LLMs) such as GPT-4 class, BERT, T5 for enterprise applications incorporating Retrieval-Augmented Generation (RAG) using vector databases and frameworks like FAISS, Elasticsearch, LangChain, LlamaIndex.
Contribute to AI platform architecture, governance, data security, and collaboration on AI/ML model lifecycle frameworks, supporting enterprise AI programs with tools like Snowflake Cortex and Databricks Lakehouse AI.
Minimum Requirements
3+ years of professional experience in AI/ML and Data Engineering.
Proficient in Python, PySpark, Scala, and advanced SQL.
Hands-on experience with AWS EMR and related cloud services for data engineering.
Work authorization or willingness to work in Pune, Maharashtra, India (location explicitly mentioned).
Ideal Candidate Profile
Experienced in deploying and operationalizing LLMs and RAG workflows in enterprise environments, indicating a strong AI engineering skillset.
Comfortable working with modern AI/cloud platforms like Snowflake Cortex and Databricks Genie for scalable AI workloads.
Capable of contributing to data platform architecture and governance, implying a strategic understanding of data security and model lifecycle management.