Prasanna Ranganathan Email & Phone Number

Bengaluru, Ka, In

Bangalore, Karnataka, In

epiFi is an early stage startup in the FinTech domain. Building the engineering team and product.

Bangalore, Karnataka, In

Flipkart Plus, Recommendations & SearchFlipkart Plus - Designed and built multiple micro-services that power the product - Building more product features and capabilitiesBuilding the next-gen Recommendations Platform - Scaling the backend data pipelines - Improve and iterate on the ML algorithmsReal-time ingestion pipeline - Kafka, Storm, Redis, HBase, Solr - Built and scaled the streaming real-time ingestion pipeline - Contributions to Apache Storm (storm-kafka-client) - 10x throughput improvement along with >2x reduction in hardware compared to previous gen - implemented comprehensive production observability (logging, metrics & alerts) - made system robust & reliable with negligible impact of instance failures - significant reduction in ingestion lag & errorsArchitected the custom next gen sharded search serving layer to handle more than 5x growth in index size. Developed & helped implement a comprehensive launch plan which included revamp of infra setup, automated build & deployment, debug tools, metric & alerts, etc.Using a data-driven approach convinced stakeholders across functions to remove/reduce data in the serving layer thereby achieving significant reduction in the capacity requirements of the search stack - indexing & serving. Drove the solution end-end to build systems/process for identifying non-critical data at the source, propagate it to all the consumers & remove such data from serving layer of search.Kickstarted the long term architecture evolution and tech debt reduction by breaking up a monolithic service.Mentor Developers; Evangelize and drive adoption of SDLC best practices - Code review, Design review, Unit/Functional/Perf tests; Ops automation

San Jose, Ca, Us

Building and Scaling a DMP + Online data-serving engine for DemandGen, Marketing & Personalization use-cases - Built data pipelines to gather raw & processed data (users/merchants/transactions) from various sources - Dataset of 400M+ User Profiles and 2.2B+ Identities. 40M+ daily updates processed - Designed the data model for segmentation and targeting use-cases - Integrated with the Ad Serving platform. Early campaigns confirmed a lift in engagement - Partnering with Data Science to build & serve models for high-value prospects, shopping propensity, etc. - Working on extending the platform for a variety of internal personalization use-cases - Working on building a reporting/analytics component to deliver in-depth insights on the data

Los Gatos, Ca, Us

Worked on the whole of the Netflix Search Backend powered by Apache Solr and Lucene.Designed & Built the new Search Aggregator Service to enable/accelerate planned future product feature testing as well as improve the performance & scalability of the overall search backend architectureDeveloped, Tested (AB testing) and Launched a bunch of successful product updates to the Netflix member search experience on the website and devices partnering closely with Product & Data Science teamsDesigned and built the complete relevance scoring algorithm powering Netflix search on game consoles, iPhone and other devices such as Roku. This was the first-ever launch of Search functionality in Netflix app (PS3 was the initial AB launch platform)Evolved the architecture through various progressions - L10N, I18N, Multi-region deployment, Active-Active, etc. as the service expanded to Canada, UK, LATAM, Europe and rest of the worldEnhanced the search index schema and relevance models to support Internationalization and Localization of searchArchitected and built the systems to run on AWS as part of Netflix's migration to the cloud; Search was among the first set of services shifted to AWS

Mountain View, Ca, Us

Design and development of Crawler, Webmap, Vertical Suggestion system.- Crawler: 5B+ docs crawled and indexed- Webmap: built using OpenMPI and Boost Parallel Graph Lib- Webmap: ran on 500-node cluster with 2B+ vertices and 4B+ edges- Vertical Suggester: Dynamic set of Verticals computed and scored at query-time; formed basis of a differentiated UX

Oakland, Ca, Us

Design, Development and Maintenance of numerous backend servers powering the search engine.

Ms, Systems Engineering

B.E., Electrical And Electronics Engineering