Vishal Bysani

Associate Consultant

Bengaluru, Karnataka, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Top 13 in JEE Advanced 2022
Experience in AI and Machine Learning
Interned at QRT and PhnyX Lab

Stackforce AI infers this person is a skilled developer with a focus on Fintech and AI applications.

Contact

Skills

Core Skills

Data ProcessingAwsMachine LearningOcaml

Other Skills

PythonAIPandasC++BashHaskellPostgreSQLNode.jsMatplotlibNumPyAutodesk Fusion 360GitJavaScriptCascading Style Sheets (CSS)HTML5

About

Hello! I am Vishal Bysani, a final year student in the Computer Science and Engineering Department at IIT Bombay. I am interested in Systems Dev, Competitive Programming, Machine Learning and much more

Experience

Indian institute of technology, bombay

Teaching Assistant

Jan 2026 – Present · 2 mos · On-site

Undergraduate Teaching Assistant in Implementation of Programming Languages (Course Code: CS 306/320)

Qube research & technologies

Quantitative Developer Intern

May 2025 – Jul 2025 · 2 mos · Mumbai, Maharashtra, India · On-site

Built a token orchestration system for Thomson Reuters Tick History (TRTH) data, distributing jobs across 35+ accounts via account pooling with on-demand token refresh, thread-safe token caching, and three scheduling strategies based on job type and priority; integrated with AWS Parameter Store for secure credential storage
Migrated a legacy intraday processor to an in-house framework, handling the end-to-end pipeline: raw data retrieval, chunk flattening, RIC-level minute resampling, and duplicate resolution

PythonAWSData Processing

Bharatgen

Undergraduate Researcher

Aug 2024 – Nov 2024 · 3 mos

Phnyx lab

AI Engineer Intern

Jun 2024 – Jul 2024 · 1 mo · San Francisco Bay Area · Remote

Studied vector indexing schemes like HNSW, product quantization, inverted file flat indexing, and DiskANN.
Implemented query optimizations such as HyDE and multi-query optimization in a RAG pipeline.
Explored evaluation benchmarks and utilized the RAGAS tool to measure faithfulness, answer relevance, and context relevance in RAG pipelines.

AIMachine Learning

Jane street

Participated in Jane Street's SEE IIT Program 2023

Dec 2023 – Dec 2023 · 0 mo · Hong Kong SAR

Learnt Jane Street's quantitative approach to trading through short lectures, mock trading games, and trading research workshops.
Learnt OCaml and the libraries and tools used in Jane Street's everyday work. These tools were utilized through various coding exercises and projects, including building the backend and UI for a computer game.

OCamlPandas