Senior Quantitative Developer and Researcher with over 7 years of experience engineering ultra-low latency trading infrastructure and sophisticated alpha-generating strategies. Proven track record in managing $8.5 Billion AUM, reducing trade processing latency by 50%, and increasing decision-making accuracy by 67%. Expertise spans the full stack of modern quantitative finance: from FPGA-accelerated market data handlers and deterministic C++ execution engines to stochastic calculus-based derivative pricing and ML-driven risk frameworks. Currently focused on sub-microsecond trading systems, kernel bypass (DPDK/Solarflare), and adaptive volatility regime execution for global HFT environments.

• Georgia Institute of Technology (Online): M.S. in Computer Science (Specialization in Computing Systems) | Expected Dec 2026
• Stevens Institute of Technology: M.S. in Financial Engineering (GPA: 3.968/4.0) | Expected May 2026
• WorldQuant University: M.S. in Financial Engineering (GPA: 86%) | 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• Carnegie Mellon University (Tepper): M.S. in Computational Finance (Program withdrawn due to father's illness) | Aug 2021 – Oct 2021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) | Sept 2018

Feb 2026 – Present | New York, USA

• Developing high-performance C++ trading systems with FPGA for Automated Market Making (AMM) strategies.
• Collaborating with front-office to optimize latency and enhance execution performance in a hybrid on-site trading environment.

Mar 2023 – Jun 2025 | Mumbai, India

• NP-Hard Optimization: Architected and developed Map Construction and Routing Algorithms solving 3 Nested NP-Hard Problems using Constraint Programming, PostGIS, and MongoDB.
• Team Leadership: Led a team of 12 engineers as Head of the Data Analytics department.
• AI/LLM Engineering: Built a Large Language Model (LLM) for internal development and query resolution, improving bug resolution efficiency by 80%.
• Data Infrastructure: Engineered a robust ML Pipeline for proactive error detection and resolution using the ELK stack.

Feb 2022 – Oct 2022 | Mumbai, India

• AUM Management: Contributed to the management of a combined AUM of $8.5 Billion.
• Alpha Generation: Developed and backtested systematic merger arbitrage strategies, yielding a 15% improvement in alpha capture.
• Execution Efficiency: Deployed scalable ML pipelines for Order & Execution Management Systems, increasing trade execution efficiency by 29%.
• Strategy Innovation: Built portfolio strategies capitalizing on arbitrage opportunities from the impact of ESG scores on pre- & post-merger statistics.

Jan 2020 – Jul 2021 | Chennai, India

• Latency Optimization: Integrated C++ pipelines in SANDRA (Object-Oriented Database) to store trades, reducing trade processing latency by 50%.
• System Migration: Led the migration of 1 million+ lines of code to Python 3.8, enhancing system scalability and execution efficiency by 40%.
• Trading Services: Engineered Python-based trading services to enhance storage, processing, and matching of trades on the QUARTZ platform.

Jun 2018 – Dec 2019 | Chennai, India

• Predictive Analytics: Architected an ML/AI platform to deploy predictive models, increasing decision-making accuracy by 67%.
• Process Automation: Designed machine learning models for data validation rules prediction, reducing manual workload by approximately 36 Full-Time Equivalents (FTEs).

• Leadership: Led and mentored cross-functional engineering teams of up to 12 members.
• Collaboration: Proficient in Agile/Scrum methodologies and hybrid/on-site trading environment collaboration.
• Problem Solving: Solving complex NP-Hard problems (e.g., nested routing algorithms) through first-principles engineering.
• Communication: Effectively bridging the gap between front-office quantitative research and core engineering execution.

• AI-Integrated FPGA for Market Making in Volatile Environments (Master's Thesis) Oct 2024 – Dec 2025 | Stevens Institute of Technology

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

• Dynamic Portfolio Optimization (Master's Thesis) Mar 2024 – June 2024 | WorldQuant University

  • Built a real-time portfolio optimization system using convex and non-convex optimization methods, enhancing risk-adjusted returns via adaptive asset rebalancing and multi-factor modeling.

• Adaptive Volatility Regime Based Execution and Risk Framework Sep 2025 – Dec 2025

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

• Statistical Arbitrage Reversal and Momentum Strategies 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.

• 1st Place: Vanguard ETF Trading Challenge (Personal Portfolio), 6th Place for the team portfolio.
• Global Recognition Gold Award (Bank of America) - Led enterprise-wide AI/ML campaign identifying 64 high-impact use cases.
• Global Recognition Silver Award (Bank of America, 2x) - For Total Return Swap Bonds contributions and AI/ML framework.
• State Rank Holder - International Science Olympiad and International Mathematics Olympiad.
• President - Stevens Graduate Financial Association.

• 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, FI, 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).

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