Shreejit Verma

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).

• 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.

• 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%.

• 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.

• 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%.

• 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).

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.

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.

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.

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.

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.

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.

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.

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

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