Ram Shankar Baskaran

GenAI & RAG Architecture: Architected an enterprise-grade Retrieval-Augmented Generation (RAG) system using Vertex AI and LangChain, reducing internal knowledge retrieval time by 40%.

Data Lakehouse Design: Led the strategic design of a multi-layered Data Lakehouse on GCP BigQuery, managing 5TB+ daily data ingestion via Cloud Composer (Airflow).

Infrastructure as Code (IaC): Standardized DevOps practices using modular Terraform scripts, reducing environment provisioning time from 3 days to 2 hours.

Team Leadership: Mentored 4 engineers on Kubernetes (GKE) deployment strategies and Python best practices, improving code quality by 30%.

Legacy Migration: Engineered the migration of 50+ legacy Alteryx workflows to a cloud-native GCP architecture using Dataflow (Apache Beam) and BigQuery, achieving $45k annual cost savings.

Agentic AI Development: Developed innovative Agentic AI workflows to automate incident reporting, utilizing LLMs to parse logs and auto-assign Jira tickets, cutting MTTR by 25%.

Real-Time Streaming: Built real-time IoT analytics pipelines using Pub/Sub and Dataflow, enabling sub-second latency for operational dashboards.

Data Governance: Implemented Dataform for SQL transformation management, introducing CI/CD version control and automated testing.

ETL Optimization: Developed and optimized robust ETL pipelines using Python and SQL, reducing BigQuery slot usage and query costs by 20% through FinOps principles.

Platform Engineering: Managed Cloud Composer upgrades and dependency management, ensuring 99.9% uptime for critical workflows.

CI/CD Automation: Built GitHub Actions pipelines to automate the deployment of Dockerized applications to Google Kubernetes Engine (GKE).