Mohit Garg

CTO

London, England, United Kingdom10 yrs 8 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in financial services software engineering.
Proficient in multiple programming languages including Python and Scala.
Strong background in deep learning and algorithmic trading.

Stackforce AI infers this person is a Fintech Software Engineer with expertise in algorithmic trading and data privacy.

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Skills

Other Skills

Deep LearningNatural Language Processing (NLP)PyTorch

About

Experienced Software Engineer with a demonstrated history of working in the financial services industry. Skilled in Python, Scala, C++, and TypeScript. Strong engineering professional with a Nanodegree focused in Deep Learning from Udacity.

Experience

Bloomberg

2 roles

Software Engineering Team Lead

Promoted

Aug 2022 – Present · 3 yrs 7 mos · London, England, United Kingdom

Senior Software Engineer

Jan 2020 – Jul 2022 · 2 yrs 6 mos · London, England, United Kingdom

Building Bloomberg's next generation buy-side OMS

Hsbc

Quantitative Developer, Rates Electronic Trading

Mar 2017 – Jan 2020 · 2 yrs 10 mos · Bengaluru Area, India

Quantitative Developer for Fixed Income (Rates) electronic trading desk

Goldman sachs

Quantitative Developer, Algo Trading Platform

Jun 2015 – May 2016 · 11 mos · Bengaluru Area, India

Primary focus on reducing the latency of the Equities Algorithmic Trading Platform.

Indian institute of technology, kanpur

Academic Tutor, Introductory Course in Programming

Jul 2014 – Apr 2015 · 9 mos · Kanpur Area, India

Tutored a weekly class of 40 first year students for two semesters.

University of california

Visiting Research Scholar

May 2014 – Jul 2014 · 2 mos · Irvine, Orange

Designed a middleware software application to protect privacy of user data on Cloud Storage Platforms. Accomplished real time encryption/decryption of user data and covered applications like Google Drive, Dropbox etc.

Ibm

Research Intern

May 2013 – Jul 2013 · 2 mos · India Research Lab, New Delhi, India

Augmented POVMiner, an application agnostic system, with atomic rules like keyword searching and dictionary matching. Used confidence measure to classify a file as configuration file with Points of Variations (POVs).
Achieved high recall (~98%) and precision (~90%) for software application servers like MySQL, Cassandra, and Apache.