Vaibhav Anand

Associate Consultant

Delhi, India5 yrs 9 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Accelerated back-tester runtimes by 90%
Generated Sharpe ratios above 2.0
Deployed production-grade ML services under 50 ms latencies

Stackforce AI infers this person is a Fintech and AI Engineering expert with a focus on quantitative research and high-frequency trading.

Contact

vaibhavanand.anand@gmail.com LinkedIn

Skills

Core Skills

Quantitative ResearchHigh-frequency TradingRisk AssessmentMachine LearningAi EngineeringQuantitative FinanceQuantitative Risk AnalysisSoftware EngineeringFrontend Development

Other Skills

Qualitative & Quantitative Research MethodologiesMarket RiskAladdinkdb+/qBloomberg EMSX/TCATradewebMarketAxessKafkaZeroMQFPGA/NIC timestampingDockerKubernetesAWS/GCPGrafanaPrometheus

About

For Any Consultation Book The Session Here https://vaibhavanand.zohobookings.in/#/278254000000042054 I am interested in Algorithmic Trading and learning about new technological advancements. Dynamic quant researcher and AI engineer with 5+ years of experience designing high‑performance trading strategies, building ML pipelines, and delivering risk analytics for global buy‑side firms. Proven track record of accelerating back‑tester runtimes by 90%, generating Sharpe ratios above 2.0, and deploying production-grade ML services under sub-50 ms latencies. ● Honors: All‑India IIT‑JEE Rank 157 Programming & Data | Python · C/C++17/20 · R · SQL · PySpark · MATLAB Machine Learning | Scikit‑Learn · PyTorch · TensorFlow · Hugging Face · OpenCV · HyperOpt Quant & Trading | Aladdin (OMS/EMS, Risk, TCA) · kdb+/q · Bloomberg EMSX/TCA · Tradeweb · MarketAxess HFT & Infrastructure | Kafka · ZeroMQ · FPGA/NIC timestamping · Docker · Kubernetes · AWS/GCP · Grafana/Prometheus Modeling & Analytics | Monte‑Carlo Simulation · VaR/ES · Options Greeks · DCF & Cash‑Flow · PPA Valuation

Experience

5 yrs 9 mos

Total Experience

1 yr 5 mos

Average Tenure

3 yrs

Current Experience

Tower research capital

Quantitative Research Analyst

Jun 2023 – Present · 3 yrs · Remote

Quant & Trading | Aladdin (OMS/EMS, Risk, TCA) · kdb+/q · Bloomberg EMSX/TCA · Tradeweb · MarketAxess
HFT & Infrastructure | Kafka · ZeroMQ · FPGA/NIC timestamping · Docker · Kubernetes · AWS/GCP · Grafana/Prometheus
Modeling & Analytics | Monte‑Carlo Simulation · VaR/ES · Options Greeks · DCF & Cash‑Flow · PPA Valuation
● End‑to‑End Options Framework: Led Python/C++ development of 0–2 DTE options strategies for NIFTY & S&P 500. Achieved 45% cumulative PnL, Sharpe 2.4, max drawdown ‑8%.
● High‑Performance Back‑tester: Optimized kdb+ and C++ multithreading, reducing 1‑year multi‑leg sim‑time from 1 hr to 4 min.
● Ultra‑Low Latency Execution: Integrated Kafka websockets with FPGA/NIC timestamping; built Grafana dashboards for sub‑µs latency monitoring.
● Risk & TCA Reporting: Automated Aladdin API calls to generate factor‑level attribution for $90 B AUM portfolios; cut stress‑test runtime by 40%.
● Leadership: Mentored 3 juniors on kdb+/q query optimization and best coding practices.

Qualitative & Quantitative Research MethodologiesMarket RiskAladdinkdb+/qBloomberg EMSX/TCATradeweb+16

Samsung electronics

Research And Development Engineer

Mar 2023 – May 2023 · 2 mos · Remote

● Developed Siamese and contrastive‑learning models for wafer defect detection; cut manual inspection time by 30%.
● Architected Slack‑integrated RAG chatbot using LangChain for fab root‑cause analysis, reducing RCA turnaround to < 1 min.

Kpmg

Artificial Intelligence Engineer

Oct 2022 – Mar 2023 · 5 mos

● Built Python Monte‑Carlo PPA valuation and scenario frameworks for solar/wind farms; advised Enel, J‑Power on hedge pay-offs.
● Led due diligence on 800 MW portfolio; crafted client decks detailing volume, regulatory, and debt‑covenant risk.
● Trained 5 associates on Bloomberg API, sensitivity analysis, and PPA modeling.

Adobe

Software Engineer

Oct 2020 – Sep 2022 · 1 yr 11 mos

● Designed OCR & font recognition pipeline for Illustrator; improved text‑extraction F1 by 18%.
● Deployed YOLOv5/ResNet gesture service on Triton + Flask, achieving 200 req/s at < 50 ms latency.
● Developed DeepStream/TensorRT facial‑ID solution for 300 k users; FAR < 0.001% with 12% FRR reduction.

Apple

Intern

May 2018 – Oct 2018 · 5 mos

● Engineered 10+ React/Redux components; Sass refinements increased render throughput by 20%.
● Architected Jest/Storybook tests, raising coverage by 20%; integrated Adobe Analytics for 25% uplift in marketing insights.