Shreyansh Kashaudhan

Software Engineer

New Delhi, Delhi, India2 yrs 10 mos experience
Highly Stable

Key Highlights

  • Led development of scalable session replay feature.
  • Engineered Kafka-based telemetry system for monitoring.
  • Achieved 94% accuracy in deep learning model integration.
Stackforce AI infers this person is a SaaS-focused Software Engineer with expertise in distributed systems and data analysis.

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Skills

Core Skills

Python (programming Language)Core JavaReact.jsKafkaData AnalysisMachine Learning

Other Skills

DynamoDBRedisKinesisHazelcastAWS S3Azure Blob StorageDeep LearningRandom ForestSVMInstitutional ResearchC++MicroservicesJava ConcurrencySpring BootKubernetes

Experience

2 yrs 10 mos
Total Experience
2 yrs 10 mos
Average Tenure
2 yrs 10 mos
Current Experience

Splunk

2 roles

Software Engineer II

Oct 2025Present · 7 mos · Bengaluru, Karnataka, India

  • - Led end-to-end design and development of a distributed Session Replay feature using DynamoDB, Redis, and Kinesis, enabling scalable session recording and retrieval, and designed session replay licensing for automated provisioning, validation, and monitoring, supporting 100+ enterprise customers.
Python (Programming Language)Core Java

Software Engineer

Jul 2023Present · 2 yrs 10 mos · Bengaluru, Karnataka, India

  • Architected and implemented a rule-based license management system using DynamoDB, enabling customers to define and enforce usage limits for Browser and Mobile apps. Developed backend logic for real-time rule evaluation and ensured reliability across workflows.
  • Spearheaded the development of an event batching framework in Hazelcast, reducing event volume by 20-30x and decreasing Kafka lag, which improved system throughput and scalability.
  • Engineered a Kafka-based telemetry system to monitor expensive ADQL queries, integrating Kafka Producers and Consumers to store query data in AWS S3/Azure Blob Storage.
  • Enhanced Sensitive Data Masking by optimizing cache design and backend logic, improving onboarding flexibility, performance, and robustness.
  • Modeled and integrated workflow states, enabling developers to define custom workflows for better debugging and insights into workflow design and execution.
  • Added support for predefined log parsers (e.g., Redis, SQL), improving parsing accuracy and simplifying developer workflows.
  • Ensured safe uninstallation of unstable solution versions during 2PC processes, maintaining object schema integrity.
Python (Programming Language)React.js

Kla

Data Scientist

Jun 2022Jul 2022 · 1 mo · Chennai, Tamil Nadu, India

  • Investigated several research papers for integrating handcrafted features in Deep Learning models to increase performance
  • Analyzed Random Forest and SVM in the combination of ADC attributes with DL features using Lasso and RF feature selection
  • Designed model to combine ADC attributes in CNN using W-Cat and DUWCat-FN with MLP to achieve accuracy up to 94%
Data AnalysisPython (Programming Language)

Indian institute of technology, kanpur

Summer Research Intern

Jun 2021Aug 2021 · 2 mos · Remote

  • Deep Learning models in communication networks
  • Analysed research papers and Machine learning model used to solve combinatorial optimisation graph problems like Travelling Salesman Problem(TSP), Convex Hull that arises in various heterogeneous domains in communication networks
Institutional ResearchMachine Learning

Education

Indian Institute of Technology, Delhi

Bachelor of Technology - BTech — Electrical and Electronics Engineering

Jul 2019May 2023

Jawahar Navodaya Vidyalaya - JNV

Class 12

Jan 2017Jan 2019

Jawahar Navodaya Vidyalaya, Banda, Uttar Pradesh

Class 6 - Class 10

Jan 2012Jan 2017

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