# Vigya Sharma

> Apache Lucene Committer and PMC member | Principal Engineer at Amazon Search

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/vigya

I'm a Principal Engineer at Amazon Search, working at the intersection of distributed systems, information retrieval, and AI-powered search. My focus is on making search more scalable, intelligent, and responsive to how people naturally express what they're looking for.

I lead initiatives that apply modern ML techniques to search quality, expand product discovery for customers early in their shopping journey, and optimize our core search infrastructure for scale and efficiency. My work spans semantic matching and ranking systems, multi-modal content understanding, and distributed systems architecture — all focused on making search more intelligent and responsive to natural language queries.

I am also a Committer and PMC member on the Apache Lucene project. You can find a living summary of my work at https://vigyasharma.github.io/about/

## Work Experience
### Principal Engineer @ Amazon
Jan 2024 – Present | Palo Alto, California, United States
Search at Amazon

### Sr. SDE at Amazon Search @ Amazon
Jan 2021 – Jan 2024 | Palo Alto, California, United States
Product Search at Amazon.

### Senior Software Engineer @ Amazon
Jan 2018 – Jan 2021 | Bengaluru Area, India
Working on resiliency, fault tolerance, cluster management, and distributed systems for Amazon Elasticsearch Service. 

Contributions (as technical lead) -
* "Autotune for Amazon ES" - a self adapting feedback loop mechanism for intelligently optimizing Elasticsearch clusters.
* Hyper-scale shard allocation and fault tolerance in Ultrawarm enabled Amazon ES clusters.
* A strongly consistent framework for in place configuration updates in a distributed system.
* Self Healing framework to auto-heal clusters.

Other projects I've helped build:
* Split brain avoidance mechanisms
* Internal monitoring systems
* Internal architecture of the service across control and data plane.
* Different parts of a domain's lifecycle supporting scaling updates and configurational changes.

I'm routinely involved in operational deep dives and mentoring other engineers.

### Founding Engineer, Amazon Elasticsearch Service @ Amazon
Jan 2014 – Jan 2021 | Palo Alto, California, United States
Part of the core team of engineers that created and launched Amazon Elasticsearch Service.

Technical Lead for:
- Cluster management and shard allocation in Amazon Elasticsearch
- Hyperscale shard allocation in Ultrawarm
- AutoTune for Amazon Elasticsearch

### Lucene Committer @ The Apache Software Foundation
Jan 2022 – Present | Palo Alto, California, United States
Committer on the Apache Lucene open source search engine. My current focus has been indexing and merging in Lucene, and I'm actively exploring other areas. I help with reviewing and committing changes, making Lucene enhancements, and maintaining the code base in general.

I implemented a concurrent, non-blocking, transactional version of the addIndexes() API, that leverages concurrent background merges to enable users to combine indexes with a low add-to-search latency. The change is foundational in unlocking the ability to decouple indexing and search shards in applications that use segment replication. 

I'll speak more about this change and decoupling indexing from search at my talk in the upcoming ApacheCon NA, '22, on October 6th at New Orleans, Louisiana - https://www.apachecon.com/acna2022/schedule.html

### Contributor @ Open Distro for Elasticsearch
Jan 2020 – Jan 2021 | Palo Alto, California, United States
Contributor and Committer for the Performance Analyzer RCA framework - the engine that powers AutoTune for Amazon Elasticsearch.

https://github.com/opendistro-for-elasticsearch/performance-analyzer-rca

### Contributor @ Elasticsearch
Jan 2019 – Jan 2020 | Palo Alto, California, United States
Contributed fixes and improvements to open source Elasticsearch.
- https://github.com/elastic/elasticsearch/pull/42066
- https://github.com/elastic/elasticsearch/pull/42658

### Member of Technical Staff @ Adobe Systems India
Jan 2013 – Jan 2014 | Noida Area, India
- Predictive Analytics on user data
- Machine learning problems to predict customer churn.
- Feature mining to extract high impact parameters.
- Deep Learning in Neural Networks.

- Big data processing and warehousing in Hadoop and Hive.
- Designed efficient ETL processes on Hive for data warehousing.
- Optimized Hive tables and data warehouse structure for fast data retrieval.

- Worked on building an analytics data warehouse ground up in SAP HANA. Initial modules live in production.


## Education
### Master of Technology (MTech) in Computer Science
Indian Institute of Technology, Delhi

### Bachelor's Degree in Computer Science
College of Technology, G.B.P.U.A & T


## Contact & Social
- LinkedIn: https://linkedin.com/in/vigyasharma
- Portfolio: https://vigyasharma.github.io/about/

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Source: https://flows.cv/vigya
JSON Resume: https://flows.cv/vigya/resume.json
Last updated: 2026-04-12