SYSTEM_READYSenior Quantitative Developer at Barclays · New York, NY

Shreejit VermaQuantitative Developer & Researcher

Quantitative Developer, Quantitative Researcher, and Quantitative Trading Engineer based in New York. I build low-latency C++ trading and risk systems: FRTB market risk engines at Barclays, automated market making at BNP Paribas, and an FPGA and kernel-bypass market-making system for my MS thesis, plus statistical arbitrage and ML-driven alpha research.

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View Experience Key Projects shreejitverma@gmail.com

$500M
daily market-making volume
C++ automated market-making stack, BNP Paribas CIB
$8.5B
AUM merger-arbitrage book
Systematic strategies, Versor Investments
50%
lower trade processing latency
C++ trade pipelines, Bank of America FICC
+20%
higher Sharpe ratio
Regime-based C++ execution framework, backtested

Professional Experience

Barclays

Oct 2026 – Present

Senior Quantitative Developer (Contract), FRTB Market Risk | New York, USA (Hybrid)

Architecting low-latency C++ and Python distributed pricing and risk calculation pipelines, optimizing Expected Shortfall (ES), Default Risk Charge (DRC), and Non-Modellable Risk Factor (NMRF) simulations across multi-asset trading desks.
Engineering high-throughput aggregation engines for the Sensitivities-Based Method (SBM), Gross Jump-to-Default (JTD), and Residual Risk Add-on (RRAO).
Designing automated backtesting and P&L Attribution (PLA) test suites (Risk-Theoretical vs. Hypothetical P&L), establishing stable model eligibility pipelines and minimizing capital charge penalties across major trading desks.
Scaling real-time scenario generation and risk factor time-series pipelines across high-performance grid environments, integrating kdb+/q and distributed message queues to ingest multi-terabyte tick and pricing feeds for risk factor observability.
Partnering with Quantitative Research, Front Office Trading, Risk Methodology, and Model Risk Governance to implement, validate, and document Basel IV compliance frameworks for regulatory audits (Fed, PRA, FINMA).

BNP Paribas CIB

Feb 2026 – May 2026

C++ Quantitative Developer (Co-op), Automated Market Making | New York, USA

Built low-latency components of the automated market-making stack for the Prime Credit Market (average $500M of daily market-making volume), spanning real-time market-data ingestion, tick analytics, and pricing/execution paths.
Profiled and optimized the software hot path feeding FPGA-accelerated market-data handlers and quoting engines.
Integrated secure on-premise LLM tooling with Git/Jira/Confluence to automate code, testing, and documentation workflows.

LogiNext Solutions Inc.

Mar 2023 – Jul 2024

Senior Software Engineer, Analytics | Mumbai, India

Architected Map Construction, Map Routing, and Rich Vehicle Routing algorithms (3 nested NP-Hard problems) using CP-SAT constraint programming and convex optimization over PostGIS, MongoDB, and S3.
Led a 12-engineer team delivering a high-throughput geospatial mapping application platform.
Built an LLM-powered debugging and query-resolution tool used company-wide, cutting mean bug-resolution time by 80%.

Versor Investments (QR Systems LLP)

Feb 2022 – Oct 2022

Quantitative Developer, Merger Arbitrage and Stock Selection Portfolio | Mumbai, India

Developed and backtested systematic merger-arbitrage strategies for an $8.5 Billion AUM fund, improving alpha capture by 15%.
Built and deployed ML pipelines for Order and Execution Management Systems, increasing trade execution efficiency by 29%.
Designed an ESG-driven merger-arbitrage signal from pre- and post-merger statistics, later run as a standalone portfolio and embedded across existing portfolios.

Bank of America

Jan 2020 – Jul 2021

Senior Software Engineer, Fixed Income Commodities and Currencies (FICC) | Chennai, India

Engineered Python-based trading services enhancing storage, processing, matching, and execution of trades on QUARTZ.
Integrated C++ pipelines to store trades in the object-oriented database SANDRA, reducing trade processing latency by 50%.
Led the migration of 1 million+ lines of code to Python 3.8, enhancing scalability and execution efficiency by 40%.

Bank of America

Jun 2018 – Dec 2019

Senior Tech Associate, Data Analysis and Insight Technology | Chennai, India

Architected and developed an ML/AI platform to deploy predictive models, increasing decision-making accuracy by 67%.
Designed machine learning models for data validation rules prediction, reducing workload by close to 36 Full-Time Equivalents (FTEs).

Research

Thesis work and research systems, each with source code or a live tool.

Featured case study

Trishul: AI-Integrated FPGA for Market Making

MS Thesis, Stevens Institute of Technology

Oct 2025 – May 2026

Sub-10us hardware-software co-designed market-making system: Verilog ITCH 5.0 parsing, L2 book, fixed-point RL inference, and single-cycle pre-trade risk on the FPGA critical path, with a zero-allocation C++20 control plane using lock-free SPSC queues and kernel-bypass style polling.

C++20VerilogFPGAKernel BypassLimit Order BookRL

Read the case study Source code

Dynamic Portfolio Optimization

MS Thesis, WorldQuant University · Mar 2024 – Jun 2024

Real-time portfolio optimization with convex and non-convex methods, adaptive rebalancing, and multi-factor modeling across interest rate, FX, credit, and market risks.

Portfolio OptimizationCVXPYFactor Models

Value Intelligence Platform

Live

Research tool · runs on this site

Reproducible value-investing screen: a deterministic multi-factor scoring engine with published formulas, weights, and limitations, golden-value tests, and CSV export.

Open the platform

Key Projects

Execution, alpha research, and engineering projects. Cards with a GitHub icon link to the source.

Adaptive Volatility Regime-Based Execution and Risk Framework

C++ library · Sept 2025 – Dec 2025

Regime-switching execution framework selecting among passive, TWAP, and aggressive execution using microstructure-robust volatility estimators, a Gaussian HMM, and Hawkes-process liquidity stress detection: +20.0% Sharpe Ratio, -6.1% transaction costs, -20.1% CVaR.

C++17Execution AlgorithmsHMMRisk

Statistical Arbitrage Reversal and Momentum Strategies

Quant Researcher, WallStreetQuants · Jun 2025 – Aug 2025

120-day volume-momentum crypto portfolio strategy: 155.76% annualized return and 1.94 Sharpe Ratio post transaction costs, outperforming the Bitcoin buy-and-hold benchmark.

PythonBacktestingAlpha Research

CryoZen: Sovereign AI Command Center

Independent project · May 2026 – Present

Self-hosted, local-first AI workspace orchestrating LLM providers behind a unified API, with MCP tool-calling agents, RAG and persistent semantic memory, and hardware-aware deployment of quantized open-weight models; nothing leaves the host by default.

LLM InfrastructureMCPRAGAgents

Plug-and-Play Agentic AI Engineering Harness

Independent project · May 2026 – Present

Cross-platform agentic developer platform on NixOS with declarative configuration, multi-agent orchestration, isolated Git worktrees, autonomous task execution, and CI-gated shipping.

NixOSMulti-Agent SystemsDevEx

ESG Merger Arbitrage Strategy

Versor Investments · Apr 2022 – Jun 2022

ESG-driven merger-arbitrage strategy capturing the opportunity created by ESG scores on target and acquirer pre- and post-merger statistics; converted into a standalone portfolio and embedded across all existing portfolios.

ESGMerger ArbitragePortfolio Strategy

Financial Modelling using Stochastic Calculus

Modeled asset prices and derivative strategies with Brownian motion, GBM, Ito's Lemma, martingales, Girsanov's theorem, SDEs, and the Fokker-Planck and Kolmogorov equations for volatility and interest rates.

Stochastic CalculusDerivatives PricingPython

Blockchain in Retail

Jan 2018 – Mar 2018

Decentralized e-commerce platform securing and streamlining retail transactions with smart contracts, currency conversion, custom hashing, and matching algorithms.

BlockchainSmart ContractsSolidity

QS Rank Predictor

Jun 2017 – Jul 2017

Ensemble of deep neural networks predicting QS World University Rankings, with suggestions on the areas each institution should improve.

Deep LearningNeural NetworksPredictive Modeling

Technical Arsenal

Low-Latency Systems

C++20/23
Lock-free data structures
Memory pools
SIMD
Cache-aware design
DPDK / kernel bypass
FPGA (Verilog, VHDL)
TCP/IP, UDP multicast
Linux kernel and performance tuning

Programming Languages

C++
Python
C
Java
R
MATLAB
kdb+/q
OCaml
JavaScript / TypeScript
Verilog
VHDL
Bash

Quantitative Finance

Market microstructure
Market making
Execution algorithms
Statistical arbitrage
Derivatives pricing
Greeks
Factor modeling
Portfolio optimization
Risk management

Market Risk

FRTB (IMA / SA)
Basel IV
Expected Shortfall
DRC / NMRF
Sensitivities-Based Method
P&L Attribution
Backtesting

Mathematics & Statistics

Stochastic calculus
Probability
PDEs
Linear algebra
Markov chains
Time series analysis
Bayesian statistics
Numerical methods
Differential equations

Machine Learning & AI

PyTorch
TensorFlow
scikit-learn
XGBoost
Random forests
RNN / LSTM
Reinforcement learning
Clustering
NLP
LLMs, RAG, MCP agents

Data & Distributed Compute

NumPy, SciPy, Polars, pandas
Spark / PySpark
Dask
Hadoop
Kafka
ZeroMQ
Airflow
PostgreSQL
MongoDB
Cassandra
Redis
InfluxDB
SQL / BQL
FastAPI, REST APIs
Slurm
IBM Symphony

Systems & DevOps

Docker
Kubernetes / OpenShift
AWS
GCP
Serverless
CMake
Git
Jenkins
Ansible
CI/CD
Linux

Engineering Philosophy

The principles behind the systems above, each tied to where it was applied.

Ultra-Low Latency
Minimizing microsecond-level overhead through kernel bypass (DPDK) and FPGA acceleration.
In practice: Trishul puts parsing, the book, inference, and risk on the FPGA critical path
Deterministic Execution
Predictable response times via lock-free data structures, pre-allocated memory, and cache-line alignment.
In practice: Zero-allocation C++20 control plane with lock-free SPSC queues
Alpha-Centric Design
Translating quantitative signals into execution strategies with minimal slippage and transaction cost.
In practice: Merger-arbitrage strategies for an $8.5B AUM fund at Versor
Scalable Architecture
Modular systems that handle millions of events per second and degrade gracefully under load.
In practice: FRTB risk pipelines on high-performance grids at Barclays
Hardware Synergies
Software shaped for modern CPUs (SIMD, branch prediction, cache hierarchy) and custom hardware.
In practice: Hot path feeding FPGA market-data handlers at BNP Paribas
Statistical Rigor
Rigorous backtesting and risk modeling so performance holds across market regimes.
In practice: Regime-switching execution framework with HMM and Hawkes models

Education

Georgia Institute of Technology (Online)

Aug 2024 – Expected Dec 2026

M.S. in Computer Science, Specialization in Computing Systems

Coursework: Computer Networks, Advanced Operating Systems, Distributed Computing, Database Management Systems.

Stevens Institute of Technology

Aug 2024 – May 2026

M.S. in Financial Engineering

GPA 3.974/4.0

Coursework: Market Microstructure, Portfolio Theory and Applications, Algorithmic Trading Strategies, Multivariate Statistics.

WorldQuant University

Dec 2021 – May 2024

M.S. in Financial Engineering

GPA 86%

Coursework: Deep Learning for Finance, Financial Econometrics, Fixed Income, Equity, Portfolio Management, Risk Management.

Carnegie Mellon University, Tepper School of Business

Aug 2021 – Oct 2021

M.S. in Computational Finance (program withdrawn due to father’s illness)

Coursework: Investments, Statistical Machine Learning, Simulation Methods, Financial Computing, Algorithmic Optimization.

Vellore Institute of Technology

Jul 2014 – Sept 2018

B.Tech in Computer Science and Engineering

GPA 8.78/10.0

Coursework: Data Structures and Algorithms, Programming Language Translators, Natural Language Processing.

GitHub Impact

Activity across public and private repositories, refreshed daily from the GitHub API. View the GitHub profile.

2,719
contributions in the last year
Across public and private repositories
2,458
commits in the last year
194 pull requests and 7 issues opened
188
pull requests merged
2 open, 5 closed without merge
4d
current contribution streak
Longest in the last year: 9 days
29
repositories committed to
66 public repositories, 189 stars, 89 forks
2017
on GitHub since
86 followers

From the GitHub API, updated October 9, 2026. Contribution counts include private repositories.

Languages

Share of commits in the last 12 months, by language

Python53.1%
JavaScript24.4%
C++11.7%
TypeScript6.1%
Shell1.9%
Java1.0%
C0.4%
Verilog0.3%
Other0.9%

Each repository's commits are split by its language mix; notebooks count as Python and markup is excluded.

Contribution streak: total contributions, current streak, and longest streak

Animated snake eating the contribution graph

Writing

October 9, 2026 · 11 min read

FRTB for Engineers: What a Market Risk Capital Engine Actually Computes

A systems view of the Fundamental Review of the Trading Book: Expected Shortfall with liquidity horizons, NMRF stress scenarios, the Default Risk Charge, the Sensitivities-Based Method, and the PLA and backtesting tests that decide whether a desk may use its internal model.

Read the article

Awards & Leadership

Awards

1st Place, Vanguard ETF Trading Challenge - Personal portfolio; 6th place for the team portfolio.
Global Recognition Gold Award, Bank of America - Led an enterprise-wide AI/ML campaign identifying 64 high-impact use cases; delivered AI/ML lectures to 2500+ employees across 4 events.
Global Recognition Silver Award, Bank of America (2x) - Total Return Swap post-trade processing contributions and an end-to-end in-house AI/ML framework.
President, Stevens Graduate Financial Association - Led the graduate finance student organization at Stevens.
Beta Gamma Sigma - International business honor society.
State Rank Holder - International Science Olympiad and International Mathematics Olympiad.