Expertise in Generative AI, LLM, Artificial Intelligence and Machine Learning, neural networks, convolutional and sequence models and knowledge of recommender systems.
Received "Wilmott Award for Excellence" in quant finance. Ranked 1st in CQF across US & Europe.
Front-Office/Quantitative Technology executive with strong technical background and skilled in managing large software and hardware projects for hedge funds.
Highly data-driven and committed to designing compute-intensive, scalable, and highly available distributed systems.
Certified in Machine Learning from Stanford University (score of 99.6%)
Implemented valuation models for equity, fixed income, credit, and energy desks.
Performed financial computations on NVIDIA GPU Tesla K80’s with about 10K parallel cores achieving performance speedup of more than 250 times.
Well versed with stochastic calculus, probability theory, numerical analysis as well as its application to the pricing of interest rate, credit, equity, commodity and FX derivatives.
Skilled in pricing techniques for exotic options using static hedging and uncertainty based models, stochastic volatility and jump-diffusion, finite difference methods and yield curve construction.
Over 20 years of experience with financial institutions and hedge funds.
Experience working with large datasets and time series analysis, linear regressions, logistic regressions, gradient descent and regularizations techniques.
Languages: C++, Python, Excel, VBA, C, C#, Perl, .Net, Java, R, MATLAB, CUDA, PyCuda, Octave
Libraries: TensorFlow, NumPy, SciPy, LLM, ChatGPT, LangChain, Pyxll, ExcelDNA, STL, Boost
Storage: Elasticache Redis, MSSQL, MySQL, Oracle, Aurora PostgreSQL, MongoDB, S3, Milvus, Chromadb
Tools: Kubernetes (k8), Docker, AWS, Azure, RDS, EC2, EKS, Rancher, MSK Kafka, VMware, git, Asana, Jenkins CICD, Meraki, Umbrella, Rook, Ceph, NVIDIA GPU, DevOps, Win/Linux, FundStudio, ITO, and Kynex, prompt engg
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