MSCS, Computing Systems, Georgia Tech | Aug 2024 – May 2026
B.Tech, Computer Science, IIT Bhubaneswar | Jul 2018 – May 2022

Email | LinkedIn | GitHub | Resume

Bio

I am currently pursuing my Master’s in Computer Science at Georgia Tech, specializing in Computing Systems with a 4.0/4.0 GPA. I completed my bachelor’s degree in Computer Science at the Indian Institute of Technology Bhubaneswar in 2022, ranking 1st among all undergraduates.

I worked as a Senior Member Technical at The D. E. Shaw Group from 2022-2024, where I led trading middleware optimization and achieved significant performance improvements. At D. E. Shaw, I achieved a 40% trading middleware performance boost and 93% reduction in Kafka application startup time. I also led UI design and mentored summer interns.

My work focuses on building high-performance, fault-tolerant systems for quantitative trading, with expertise in performance engineering, multithreading, Java Native Interface (JNI), and distributed systems including Kafka and Solace.

Skills

  • Programming: C++, Java, Python, SQL, JavaScript
  • Systems & Infrastructure: Distributed systems (Kafka, Solace), Databases, Operating Systems, Cloud (AWS, Azure)
  • Performance Engineering: Multithreading, Java Native Interface (JNI), benchmarking (JMH)
  • Machine Learning Systems: Model compression, retrieval-augmented generation (RAG), PyTorch
  • Software Engineering Practices: System design, CI/CD, testing (JUnit, Mockito), Unix/Linux environments

Experience

Barclays Investment Bank (Statistical Modelling and Development)

Quant Dev Intern | New York, US | Jun 2025 – Aug 2025

  • Improved p99 latency by 23% by profiling the Java quoting pipeline with JMH, diagnosing cross-socket NUMA traffic in a JNI-backed C++ library, and optimizing synchronization
  • Increased throughput by 2.6× and cut message size by 50% by migrating inter-process communication from string-encoded messages to Protocol Buffers
  • Designed a real-time event stream to convert yield-space quotes to price-space valuations to have a unified fair price

The D. E. Shaw Group (Middleware team)

Senior Member Technical | Hyderabad, India | Jan 2024 – Jul 2024

  • Decreased ops workload by 30% by enhancing recovery of partial transaction log files and adding integrity checks against distributed replica warehouses
  • Boosted trading middleware performance by 40% by optimizing cryptography implementation with OpenSSL and Java Native Interface, eliminating garbage generation
  • Led the UI design of the intern evaluation portal and a UI overhaul of the monitoring tool based on user feedback
  • Guided and mentored two summer interns through the completion of the internship

Member Technical | Hyderabad, India | Jun 2022 – Jan 2024

  • Reduced start-up time of stateful Kafka applications by 93% by engineering a Kafka Application Recovery Protocol
  • Co-presented Kafka Application Recovery Protocol at the Great International Developer Summit in Bengaluru
  • Created a Slack bot that allows users to run custom scripts securely with user credentials through Slack commands, earning Runner-up at Infinity Hacks 2022, a company-wide 24-hour hackathon, among 350 participants
  • Set up system infra such as load balancers, CNAMEs, Kerberos, CI/CD, and CRONs for 15+ services

Software Developer Intern | Hyderabad, India | May 2021 – Jul 2021

  • Developed an all-in-one monitoring and debugging tool for Kafka users in the firm, providing a convenient way of analyzing topics, metrics, brokers, and consumer groups of Kafka clusters

Projects

Classmate AI

React Native, Flask, LLM, RAG, Python Celery, Azure, Full-stack, Figma

  • Built a cross-platform app for real-time transcription, AI summaries, and Q&A, improving lecture review efficiency by 30% in pilot tests (50+ students)
  • Architected a fault-tolerant Whisper + LLM pipeline and ran A/B tests on note layouts and Q&A interface, showing 41% higher engagement and 2× faster response times

RAG Pipeline for Textbook QA

Python, FAISS, Sentence Transformers, Chunking, RAG

  • Designed a retrieval-augmented generation (RAG) system that indexes 1,000+ textbook pages into FAISS with dense + BM25
  • Implemented DocETL style query planning, ensemble re-ranking, and structured logging, visualization, with support for llama.cpp inference and document chunking

Publications

  • Presented Tree Gradient Coding Considering Communication Delays and Partial Stragglers at IEEE ICC Denver, CO, 2024
  • Presented Improved Tree Gradient Coding with Non-uniform Computation Load at IEEE ICC, Montreal, QC, 2021

Contact

Ready to discuss opportunities and collaborations.

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Building the future, one system at a time.