---
title: "Revadi Sai Eswar - SDE-2 in Bengaluru | Tal"
description: "Meet Revadi Sai Eswar, SDE-2 @ Ola | Building Scalable Search Solutions based in Bengaluru. AI stack includes antigravity, claude, and codex."
image: "https://tal.club/meet/revadi-sai-eswar/share-image"
---

# Revadi Sai Eswar on Tal

## Headline

SDE\-2 @ Ola \| Building Scalable Search Solutions

## About

Software Engineer with experience building large\-scale search systems and low\-latency APIs\. At Ola Maps, I work on designing and improving search infrastructure powering place discovery and navigation experiences\. My work focuses on improving search relevance, ranking quality, and query recovery for large\-scale search systems\. I have worked on building and optimizing search pipelines including autocomplete ranking, query parsing, spell correction, and fallback retrieval mechanisms\. I also developed an ML\-based ranking model using XGBoost for autocomplete and implemented popularity\-based ranking strategies that significantly improved search accuracy\. Some of the areas I work on include: \- Search ranking and relevance tuning \- Query parsing and spelling correction systems \- Low\-latency backend APIs for search and geospatial services \- Search accuracy testing and observability pipelines Tech stack: Node\.js, Python, Elasticsearch, PostgreSQL, Redis\. I’m particularly interested in building scalable backend systems and solving complex engineering problems in distributed systems\.

## Location

Bengaluru

## AI stack

antigravity

claude

codex

cursor

opencode

claudecode

## Work experience

SDE\-2 at Ola, Aug 2024 \- Present · 2 yrs 1 mo, On\-site, • Developed and integrated an ML\-based XGBoost ranking model for the Autocomplete pipeline, improving overall search accuracy by 4%\. • Implemented popularity\-based ranking strategies for the Autocomplete Search API, improving search accuracy by 12%\. • Built a distributed spelling correction service to recover results from misspelled or incomplete queries, improving null query match rates by nearly 50%\. • Designed fallback retrieval mechanisms using Elasticsearch fuzziness to reduce null search outcomes by 60%\. • Led improvements to monitoring and alerting for search services, tracking SLIs such as latency, throughput, and error rate\. • Designed the high\-level architecture for a disaster recovery system to improve reliability and fault tolerance of the search service\.

SDE at Ola, Jun 2022 \- Jul 2024 · 2 yrs 2 mos, Bengaluru, • Designed and developed core search APIs including Nearby Search, Text Search, Forward Geocoding, and Search Along Route for the Ola Maps platform\. • Built a Street Lookup feature to retrieve the nearest street for a given location, improving reverse geocoding experience by 70%\. • Developed a Point\-In\-Polygon API to extract address components for a location using tokenization and aggregation strategies\. • Built an automated search accuracy testing pipeline using Airflow to validate search relevance after code or data changes\. • Refactored the Node\.js service from ES5 to ES6, improving code maintainability and reducing service latency by \~25%\. • Built internal CRUD tools using Next\.js, PostgreSQL, and Elasticsearch for efficient POI management by operations teams\.

## Education

Bachelor's degree, Computer Science, Motilal Nehru National Institute of Technology

[View the original profile](https://tal.club/meet/revadi-sai-eswar)

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