Quantitative Developer and Researcher based in New York, engineering ultra-low latency trading 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 across the Internal Model Approach (IMA) and Standardised Approach (SA). Previously a C++ Quantitative Developer at BNP Paribas CIB (Automated Market Making), building low-latency components of the market-making stack for the Prime Credit Market (average $500M daily market-making volume). Previously contributed to systematic merger-arbitrage strategies at an $8.5 Billion AUM fund and engineered FICC trading services at Bank of America, reducing trade processing latency by 50%. Expertise spans the full stack of modern quantitative finance: FPGA-accelerated market data handlers, kernel bypass (DPDK), lock-free C++ execution engines, stochastic calculus-based derivative pricing, and ML-driven risk and execution frameworks.

• 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• Carnegie Mellon University (Tepper): M.S. in Computational Finance (Program withdrawn due to father's illness) | Aug 2021 - Oct 2021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

Oct 2026 - Present | New York, USA (Hybrid)

Optimizing enterprise-scale analytics engines and distributed calculation pipelines supporting the Fundamental Review of the Trading Book (FRTB) across the Corporate & Investment Bank. Leading technical delivery of Basel IV market risk capital models across the Internal Model Approach (IMA) and Standardised Approach (SA).

• Engine Architecture & HPC: 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.
• Sensitivities & SA-TB: Engineering high-throughput aggregation engines for the Sensitivities-Based Method (SBM), Gross Jump-to-Default (JTD), and Residual Risk Add-on (RRAO).
• IMA & P&L Attribution: 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.
• Large-Scale Data Architecture: Scaling real-time scenario generation and risk factor time-series pipelines across high-performance grids, integrating kdb+/q and distributed message queues to ingest multi-terabyte tick and pricing feeds for risk factor observability.
• Model Validation & Governance: 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).
• Tech Stack: C++20, Python (NumPy, SciPy, Polars), kdb+/q, HPC grids (Slurm, IBM Symphony), multi-threading/SIMD, Docker, OpenShift/Kubernetes, Git, CI/CD.

Feb 2026 - May 2026 | 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.

Mar 2023 - Jul 2024 | 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%.

Feb 2022 - Oct 2022 | 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 capitalizing on pre- and post-merger statistics.

Jan 2020 - Jul 2021 | Chennai, India

• Engineered Python-based trading services enhancing storage, processing, matching, and execution of trades on QUARTZ.
• Integrated C++ pipelines with 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%.

Jun 2018 - Dec 2019 | Chennai, India

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

• Mathematics & Statistics: Probability, Stochastic Calculus, Differential Equations, PDE, Linear Algebra, Numerical Methods, Markov Chains
• Quantitative Finance: Statistical Analysis, Derivative Pricing, Time Series Analysis, Factor Modeling, Predictive Modeling, Greeks, Market Microstructure, FRTB (IMA/SA), Basel IV Market Risk, Expected Shortfall, P&L Attribution
• Machine Learning: Linear Regression, Clustering, Random Forest, XGBoost, RNN, LSTM, Deep Learning, Neural Networks, NLP, LLMs
• Programming: C++ (17/20/23, primary), Python, C, Java, R, MATLAB, JavaScript, Node.js, ReactJS, NumPy, Pandas, Polars, SciPy, Keras, PyTorch, TensorFlow, Scikit-learn, QuantLib, Statsmodels, CVXPY, OpenMP, MPI, CUDA, Bash
• Data Engineering: Airflow, Dask, Spark, PySpark, FastAPI, Kafka, Flink, SQL, BQL, KDB+/Q, PostgreSQL, MongoDB, ZeroMQ, Cassandra, Redis, Hadoop, HDFS
• Systems & Low Latency: TCP/IP, UDP, Multicast, cache and multithreading optimization, SIMD, FPGA (Verilog, VHDL), kernel bypass (DPDK), lock-free data structures, HPC grids (Slurm, IBM Symphony)
• Cloud & DevOps: Linux, Git, Jenkins, CI/CD, Ansible, Docker, Kubernetes, OpenShift, Helm, AWS, GCP

Oct 2025 - May 2026

• Engineered a sub-10us low-latency trading system with a custom-built limit order book, FPGA market data handlers, kernel bypass (DPDK), hardware timestamping, and lock-free data structures for deterministic, microsecond-level execution.

May 2026 - Jul 2026

• Architected a local-first, autonomous agentic AI platform orchestrating LLM providers behind a unified API, enabling agents to autonomously execute multi-step tool-calling workflows via the Model Context Protocol (MCP) with zero data leaving the host.
• Developed a RAG and persistent semantic-memory system on ChromaDB using high-dimensional vector embeddings for low-latency retrieval, plus a hardware-aware layer that deploys quantized open-weight models.

May 2026 - Jul 2026

• Architected a cross-platform agentic AI developer platform on NixOS with declarative configuration, version-pinned dependencies, symlink-managed files, and automated health checks to bootstrap a complete local AI engineering environment on clean machines.
• Integrated multi-agent orchestration and agent-native CLI workflows automating isolated Git worktrees, autonomous task execution, CI-gated shipping, overnight runs, and upstream synchronization.

Sept 2025 - Dec 2025

• Developed an adaptive volatility regime-switching framework dynamically selecting among passive, TWAP, and aggressive execution strategies.
• Achieved a 20.0% increase in Sharpe Ratio, 6.1% transaction cost reduction, and 20.1% CVaR decrease with robust risk management.

Jun 2025 - Aug 2025

• Designed and backtested a 120-day volume-momentum-based crypto portfolio strategy, yielding a 155.76% annualized return and 1.94 Sharpe Ratio (post transaction costs), significantly outperforming the Bitcoin buy-and-hold benchmark.

Mar 2024 - Jun 2024

• Built a real-time portfolio optimization system using convex and non-convex optimization, enhancing risk-adjusted returns via adaptive asset rebalancing and multi-factor modeling across interest rate, FX, credit, and market risks.

Apr 2022 - Jun 2022

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

• 1st Place - Vanguard ETF Trading Challenge (personal portfolio).
• President - Stevens Graduate Financial Association.
• Beta Gamma Sigma Member - International business honor society.
• Global Recognition Gold Award (Bank of America) - Led enterprise-wide AI/ML campaign identifying 64 high-impact use cases; delivered AI/ML lectures to 2500+ employees across 4 large-scale events.
• Global Recognition Silver Award (Bank of America, 2x) - Total Return Swap contributions (Post Trade Processing) and an end-to-end in-house AI/ML framework.
• State Rank Holder - International Science Olympiad and International Mathematics Olympiad.

• CFA Level 1
• Bloomberg Market Concepts (BMC)
• Financial Engineering and Risk Management Part I & II (Columbia/Coursera)
• Investment Foundations Program (CFA Institute)
• The Complete Financial Analyst Training & Investing Course
• Machine Learning for Trading Specialization (Google Cloud/NYIF)
• Investment Management Specialization (Geneva/UBS)
• Trading Strategies in Emerging Markets Specialization (ISB)
• Finance & Quantitative Modeling for Analysts (Wharton)
• Corporate Finance and Valuation (NYU Stern, Aswath Damodaran)

• Deep Learning Specialization (Andrew Ng/Coursera)
• Applied Data Science with Python Specialization (Michigan)
• Data Science Foundations using R Specialization (Johns Hopkins)
• Data Science Statistics and Machine Learning Specialization (Johns Hopkins)
• Big Data Specialization (UC San Diego)
• Data Structures and Algorithms Specialization (Coursera)
• Algorithms, Part I & II (Princeton)

• Interests: Chess, Poker, F1, Martial Arts, Cricket, Boxing, Badminton, Reading, Cooking, Dancing, Psychology, History, Philosophy.
• Languages: English, Hindi (Fluent); French, Sanskrit, Spanish, Russian (Intermediate); Chinese, Italian, Tamil, Punjabi (Beginner).

This repository also powers www.shreejitverma.com (Next.js 16, Tailwind CSS v4, deployed on Vercel).

npm run dev          # local dev server
npm run build        # production build
npm run lint         # ESLint
npm run typecheck    # TypeScript strict check
npm run test:e2e     # full Playwright E2E suite (desktop + mobile)

The E2E suite (e2e/) covers every public page; e2e/README.md lists what each spec checks. CI runs lint, typecheck, and the full suite on every pull request (.github/workflows/e2e.yml). scripts/enrich_books.py enriches the reading-list dataset with cover images and descriptions from Open Library and Google Books; it is resumable and idempotent. scripts/import_vault_covers.py imports curated covers from the Obsidian vault (Reading/Library/ notes and Attachments/Covers/) as 320px WebP files under public/covers/, matching books by title and author and never guessing: ambiguous matches, generated title-card covers, and titles listed in scripts/cover_overrides.json are skipped. Adding a title to that skip list also clears a vault cover it already received, so enrich_books.py fetches a hotlinked one on its next run. Vault covers take precedence over the hotlinked covers from enrich_books.py, which never overwrites an existing cover; rerun the import after adding books or covers to the vault (python3 scripts/import_vault_covers.py --dry-run previews the changes). scripts/make_icons.py renders the "SV" monogram site icons (app/favicon.ico, app/icon.png, app/apple-icon.png); rerun it with uv run --with pillow python3 scripts/make_icons.py after changing the theme's primary color. .github/workflows/metrics.yml refreshes the GitHub Impact cards daily and publishes them to the output branch: scripts/github_stats.py writes github-stats.json (rendered natively on the home page), scripts/github_cards.py draws the README's stats and language cards from it, and python3 -m unittest discover -s scripts/tests tests both. scripts/categorize_books.py assigns book categories through hand-curated overrides (scripts/category_overrides.json), title-family regex rules, and ordered keyword rules, keeping the General shelf a small miscellany bucket. scripts/ also holds standalone personal data tooling unrelated to the site, such as scripts/amazon_ir_scraper.py, which downloads Amazon investor-relations PDFs (annual reports, proxy statements, shareholder letters) into ~/Downloads/Amazon_IR_Documents. backlog.md is the workspace task backlog managed by tasks-axi.

Refreshed daily by .github/workflows/metrics.yml from the GitHub API, including private-repository activity.

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