Dr. Ranveer Agrawal, Ph.D.

Software Engineer

New Delhi, Delhi, India5 yrs 5 mos experience

Highly Stable

Key Highlights

Ph.D. in Computational Finance from MIT
Delivered $100M+ annual PnL in trading systems
Expertise in ultra-low-latency trading infrastructure

Stackforce AI infers this person is a Fintech expert specializing in ultra-low-latency trading systems and quantitative research.

Contact

Skills

Core Skills

Ultra-low-latency System DesignFull-stack Trading InfrastructureSignal Research

Other Skills

C++20DPDKReinforcement LearningReal-time Data IngestionOCamlFPGALatency OptimizationStatistical ArbitrageEnsemble BoostingTensorRTSignal Pipeline DevelopmentKDB+/qCointegrationHMMPython

About

I'm a Quant Developer with a rare mix of mathematical depth, low-level programming excellence, and high-frequency trading intuition. I started by cracking IIT-JEE with AIR-399, then went on to win medals at IMO and IOI, reach the ICPC World Finals, and complete a Ph.D. in Computational Finance from MIT. I’ve built systems that execute trades in sub-microsecond latency, delivered $100M+ annual PnL, and researched RL-based market-making strategies. I specialize in: Ultra-low-latency system design (C++, Rust, FPGA) Signal research (stat arb, ML alpha models) Market microstructure modeling Full-stack trading infrastructure Formerly at Jane Street, Two Sigma, and D. E. Shaw. Currently leading quant infra at Citadel Securities. Let’s connect over latency, alpha, or algebraic topology.

Experience

5 yrs 5 mos

Total Experience

5 yrs 5 mos

Average Tenure

5 yrs 5 mos

Current Experience

Citadel securities

Senior Quant Developer – Ultra-Low Latency Trading

Jan 2021 – Present · 5 yrs 5 mos · Florida, United States

Architected sub-microsecond order execution system using C++20 and DPDK
Led the rebuild of options MM infra → boosted PnL by $35M in 1 year
Integrated reinforcement learning agents for adaptive quoting
Worked closely with traders to build alpha features from microstructure signals
Built real-time data ingestion from 200+ exchanges globally

C++20DPDKReinforcement LearningReal-time Data IngestionUltra-low-latency system designFull-stack trading infrastructure

Jane street

Quant Developer Intern (Return Offer)

Jun 2020 – Aug 2020 · 2 mos · New York, United States

Developed pricing engine for bonds & options in OCaml
Accelerated MM strategies with custom FPGA NIC integrations
Worked on latency-sensitive path optimizations in exchange connectivity
Return offer extended, declined for Citadel full-time

OCamlFPGALatency OptimizationFull-stack trading infrastructure

Two sigma

Quant Research Intern – Alpha Modeling & ML

Jun 2018 – Aug 2018 · 2 mos · New York, United States

Built a statistical arbitrage model using ensemble boosting techniques
Developed signal pipeline from raw alternative data (satellite imagery + footfall)
Reduced training runtime by 50% using custom TensorRT pipeline

Statistical ArbitrageEnsemble BoostingTensorRTSignal research