Shubham Jamuar

Dhanbad.

About

Results-driven software developer with 1.5+ years of experience in Java, Python, and Scala. I design scalable, maintainable systems with a focus on efficiency and resource optimization. With a strong foundation in algorithms, data structures, and distributed systems, I uphold high standards in all projects. Proficient in Amazon Web Services (AWS), I take ownership of my work and am committed to delivering exceptional results.

Work

Amazon
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Software Development Engineer

Summary

Featured Merchant Algorithm, Context Aware Precompute • Export Consistency Service - Workflow for measuring consistency of Featured Offers of Exports use-case in our data stores • Designed a robust sampling strategy to obtain a list of ASINs to ensure fair representation of ASINs for workflow to run upon. • Designed and implemented the workflow to compute consistency of our featured offers in exports use-case with a latency reduced by 50 ms on an average by the use of caching over external components which vend static configuration • Deployed the workflow as a long running Fargate service on AWS ECS and set up corresponding monitors and alarms • All Buying Option Support - Project to support multiple buying options in our precompute system, which increased the accuracy of our precompute system by 1.4% • Designed an enhanced ASIN sampling strategy to enable testing of changes in order to support various buying options with a fallback mechanism to ensure representation of ASINs having buying option type with very low view traffic • Onboarded static configuration required to fetch different buying options to Amazon internal config store and developed the client required to fetch these configurations. • Enhanced the logic of accuracy computation in order to incorporate the support of different buying option types, • Segments Data Publisher - Service to publish data from S3 bucket to Amazon internal data store • Created an end to end resilient workflow from using AWS Step Functions and AWS ECS to publish segments data from S3 to our data store • Implemented multi-threading for optimal usage of the ECS Fargate computing allowing to publish data at a rate of 20000 TPS • Implemented checkpointing mechanism to ensure smart handling in case of ECS task failure and subsequent retry with Step Functions Search Relevance, Non-Default Sort • Ranking Model Evaluation Framework - Framework to evaluate various ranking models in production used to rank search results of customer queries • Engineered the framework from end to end with various AWS technologies like AWS Step Functions, AWS Lambda and AWS EMR Serverless to calculate various metrics and dump the result in a S3 bucket • Integrated the framework with a Query Replay Service via collaborating with multiple teams to automate extraction of customer queries with a high TPS and used the extracted logs for computation of various metrics required, reducing the manual effort required in evaluation by Tk person-hours • Configuration for A/B Testing of Ranking Models • Worked on configuration for A/B testing and launch of various ranking models in production, with a projected annualized impact of more than $1,000,000,000 worldwide in 2023

Amazon
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Software Development Engineer Intern

Summary

Traffic and Marketing Technologies, Creator Event Services • Worked on implementation of package to pull customer click logs from Amazon internal log store and dump into S3 • Created infrastructure via AWS CDK to deploy JAR to S3 to be used by AWS EMR to extract logs

Education

Indian Institute of Technology (Indian School of Mines), Dhanbad

Bachelor of Technology

Computer Science and Engineering

Grade: 8.57

Skills

C++
Java
Scala
Spark
Python
Amazon Web Services (AWS)
Distributed Systems
CI/CD Pipelines
Object Oriented Programming
Machine Learning/Deep Learning
Data Structures and Algorithms
Linux
DBMS
SQL

Projects

Colour Image Quantization

Summary

Implemented KNN on frames of video to reduce the number of colours used in a frame to 'k' and maintained a global colour palette to attribute for all the frames in a video. This was done to achieve compression of in-memory space occupied by a particular frame