--- title: "Resume" description: "Full resume as a web page, including certifications" url: "https://www.shreejitverma.com/resume" author: "Shreejit Verma" --- # Shreejit Verma Quantitative Developer | Quantitative Researcher | Quantitative Trading Engineer Senior Quantitative Developer at Barclays · New York, NY [Download PDF](https://www.shreejitverma.com/Shreejit_Verma_Resume.pdf) · [\[email protected\]](https://www.shreejitverma.com/cdn-cgi/l/email-protection#a1d2c9d3c4c4cbc8d5d7c4d3ccc0e1c6ccc0c8cd8fc2cecc) · [GitHub](https://github.com/shreejitverma) · [LinkedIn](https://www.linkedin.com/in/shreejitverma/) · [shreejitverma.com](https://www.shreejitverma.com/) ## Summary Quantitative Developer and Researcher engineering low-latency trading and risk infrastructure and alpha-generating strategies for hedge funds, proprietary trading, and high frequency trading environments. Currently a Senior Quantitative Developer at Barclays, building C++ and Python FRTB market risk engines for Basel IV capital (IMA and SA). Previously built C++ automated market-making components at BNP Paribas CIB for the Prime Credit Market, developed systematic merger-arbitrage strategies at an $8.5B AUM fund, and built FICC trading services at Bank of America. ## Professional Experience ### [Barclays](https://www.ib.barclays/) Oct 2026 - Present Senior Quantitative Developer (Contract), FRTB Market Risk | New York, USA (Hybrid) Optimizing enterprise-scale analytics engines and distributed calculation pipelines for the Fundamental Review of the Trading Book (FRTB) across the Corporate & Investment Bank, leading technical delivery of Basel IV market risk capital models under the Internal Model Approach (IMA) and Standardised Approach (SA). - 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](https://cib.bnpparibas/) 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.](https://loginextsolutions.com/) 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)](https://versorinvest.com/) 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](https://www.bankofamerica.com/) 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](https://www.bankofamerica.com/) 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). ## Education - Georgia Institute of Technology (Online) · M.S. in Computer Science, Specialization in Computing Systems · Aug 2024 - Expected Dec 2026 - Stevens Institute of Technology · M.S. in Financial Engineering, GPA 3.974/4.0 · Aug 2024 - May 2026 - WorldQuant University · M.S. in Financial Engineering, GPA 86% · Dec 2021 - May 2024 - Carnegie Mellon University, Tepper School of Business · M.S. in Computational Finance (program withdrawn due to father’s illness) · Aug 2021 - Oct 2021 - Vellore Institute of Technology · B.Tech in Computer Science and Engineering, GPA 8.78/10.0 · Jul 2014 - Sept 2018 ## Skills - 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 ## Research & Selected Projects ### [Trishul: AI-Integrated FPGA for Market Making](https://github.com/shreejitverma/trishul-ultra-hft-project) Oct 2025 - May 2026 MS Thesis, Stevens Institute of Technology 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. [Read the case study](https://www.shreejitverma.com/work/trishul) ### [Adaptive Volatility Regime-Based Execution and Risk Framework](https://github.com/shreejitverma/Adaptive-Volatility-Regime-Based-Execution-and-Risk-Framework) Sept 2025 - Dec 2025 C++ library 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. ### [Statistical Arbitrage Reversal and Momentum Strategies](https://github.com/shreejitverma/Statistical-Arbitrage-Reversal-and-Momentum-Strategies) Jun 2025 - Aug 2025 Quant Researcher, WallStreetQuants 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. ### [Dynamic Portfolio Optimization](https://github.com/shreejitverma/Dynamic-Portfolio-Optimization) Mar 2024 - Jun 2024 MS Thesis, WorldQuant University Real-time portfolio optimization with convex and non-convex methods, adaptive rebalancing, and multi-factor modeling across interest rate, FX, credit, and market risks. ### [CryoZen: Sovereign AI Command Center](https://github.com/shreejitverma/srijan) May 2026 - Present Independent project 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. ### [Plug-and-Play Agentic AI Engineering Harness](https://github.com/shreejitverma/dotfiles-nix) May 2026 - Present Independent project Cross-platform agentic developer platform on NixOS with declarative configuration, multi-agent orchestration, isolated Git worktrees, autonomous task execution, and CI-gated shipping. ### ESG Merger Arbitrage Strategy Apr 2022 - Jun 2022 Versor Investments 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. ### 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. ### 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. ## Achievements & Certifications - 1st Place, Vanguard ETF Trading Challenge - Global Recognition Gold Award, Bank of America - Global Recognition Silver Award, Bank of America (2x) - President, Stevens Graduate Financial Association - Beta Gamma Sigma - State Rank Holder ### Certifications #### Finance - [CFA Level 1](http://basno.com/l2c3uqav) - [Bloomberg Market Concepts (BMC)](https://portal.bloombergforeducation.com/certificates/8Nm9y3yx5b9yaWztgxSmewLD) - [Financial Engineering and Risk Management Part I & II (Columbia)](https://coursera.org/share/70b94743c090c143954cdcbe01ebf521) - [Investment Foundations Program (CFA Institute)](http://basno.com/cfbvwwwf) - [Machine Learning for Trading Specialization (Google Cloud / NYIF)](https://coursera.org/share/7a85fee31445a626d1212f7c2f55eeab) - [Investment Management Specialization (Geneva / UBS)](https://www.coursera.org/specializations/investment-management) - [Trading Strategies in Emerging Markets (ISB)](https://www.coursera.org/specializations/trading-strategy#courses) - [Finance & Quantitative Modeling for Analysts (Wharton)](https://coursera.org/share/abe2916c68432d5d156494c2f1f59b6d) - [Corporate Finance and Valuation (NYU Stern, Aswath Damodaran)](https://coursera.org/share/ad6c6e3574db71e2e38777b74b6af97f) - [The Complete Financial Analyst Training & Investing Course](https://www.udemy.com/course/the-complete-financial-analyst-training-and-investing-course/) #### Computer Science - [Deep Learning Specialization (Andrew Ng)](https://coursera.org/share/62c6f8a2d4a998dc4856249a1a937e17) - [Algorithms, Part I & II (Princeton)](https://www.coursera.org/learn/algorithms-part1) - [Data Structures and Algorithms Specialization](https://coursera.org/share/98a957a6518b5ca605f44f365df05151) - [Applied Data Science with Python (Michigan)](https://coursera.org/share/a24e1310f62486c32f6a2393fa1240dc) - [Data Science Statistics and Machine Learning (Johns Hopkins)](https://www.coursera.org/specializations/data-science-statistics-machine-learning) - [Data Science Foundations using R (Johns Hopkins)](https://coursera.org/share/3ca9e040262f60d9c367379013a1e7c1) - [Big Data Specialization (UC San Diego)](https://coursera.org/share/e3c471b726d96029d683efcfec957692)