Rahul
Talari
Building enterprise-scale data platforms, real-time pipelines, and SIEM detection systems at TCS. Passionate about clean data, intelligent systems, and research that matters.
Gayatri Vidya Parishad College of Engineering (Autonomous)
Tata Consultancy Services
Jun 2024 – Present- Lead end-to-end SIEM use case development — translating stakeholder requirements into scalable Splunk detection logic and dashboards.
- Designed optimized advanced SPL queries and correlation searches for real-time incident detection.
- Built internal Splunk Agentic automation tool reducing engineering effort by ~40%.
- Reduced MTTR by ~30% and improved dashboard load performance by ~35% via query tuning and indexing optimization.
- Built monitoring use cases across Documentum, Control-M, Tech-MQ, IIS, and HPC systems.
AICTE RPS Funded Project
Apr 2022 – Mar 2024- Designed ETL pipelines for machine-generated data; conducted exploratory analysis with rich visualizations.
- Deployed 3 ML models using MLOps practices; collaborated across IoT and mechanical engineering domains.
- Published findings in a Springer SCI-indexed journal under the AICTE RPS initiative.
Enterprise Restaurant Analytics Lakehouse Platform
End-to-end real-time and batch Lakehouse for a multi-location restaurant chain. Medallion architecture (Bronze/Silver/Gold) with Delta Lake, CDC ingestion from Azure SQL, real-time streaming via Event Hubs, Galaxy schema for KPIs, and AI/BI dashboards powered by Databricks Mosaic AI for sentiment analysis.
Real-Time Air Quality Data Platform
Cloud pipeline processing 10M+ records with automated ELT, Star Schema modeling, Streamlit dashboards, and a data quality framework that reduced inconsistencies by 95%. Query performance improved 80% via dynamic tables and aggregated views.
Intelligent Healthcare Diagnosis System
Symptom-based disease diagnosis using LLMs, RAG, Neo4j Knowledge Graphs, and Milvus VectorDB. Achieved 97% evaluation performance with significantly reduced hallucination rates.
ML algorithm for efficient damage localization in composite structures using Random Forest
Crack localization in glass fiber composite beams via Experimental Modal Analysis and Gaussian Process Regression
ML-enabled crack diagnosis and prognosis in Glass/Carbon Fiber composite structures
Random Forest regression model for crack localization in composite beams
→ View PaperApplication No. 202441101769 A
Application No. 202441048458 A
& Innovation collaborations. 👋
Looking for opportunities and collaborations in AI/ML · Data Engineering · Cloud (AWS, Azure) · Innovation & Research. Feel free to reach out through any channel below.