Umesh Tekwani

Operations Associate

Mumbai, Maharashtra, India0 mo experience

Key Highlights

  • Expert in Python and low latency applications.
  • Proven track record in fintech project delivery.
  • Strong background in parallel computing and algorithm optimization.
Stackforce AI infers this person is a Fintech professional with expertise in low latency trading applications and algorithm optimization.

Contact

Skills

Core Skills

PythonLow LatencyC

Other Skills

Artificial Neural NetworksC++C++ LanguageCrucibleEclipseElectronicsJIRAMPIMatlabMicrosoft OfficeObject Oriented DesignOpenMPPTFPerforceTWiki

Experience

0 mo
Total Experience
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Average Tenure
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Current Experience

Morgan stanley

Associate

Aug 2013Present · 12 yrs 10 mos · Mumbai Metropolitan Region

  • Working in a very dynamic environment – coordinating with global team members, meeting short deadlines and handling production outages with huge trading exposure.
  • Delivered a project to incorporate reporting feature in our “Risk Controls library” which essentially reports all the risk and regulatory related checks applied on all orders.
  • Developed same feature as above in our low latency application (velocity plant) which is used by high frequency trading applications.
  • Developed an application (Dynamic Allocator) to manage each client’s total trading limit among various trading instances, by intelligently distributing based on client’s current utilization on each instance.
  • Used Python and Google’s unit testing framework for unit testing of developed features.
PythonLow LatencyObject Oriented DesignElectronics

Samsung electronics

Internship

May 2012Jul 2012 · 2 mos · Bengaluru, Karnataka, India

  • Worked on a smartphone application to classify emotional state of a user via audio-visual input.
  • Developed an engine in C to implement the feature extraction and classification algorithm.
C

Kaist

Intern

May 2011Jul 2011 · 2 mos · Jung District, Daejeon, South Korea

  • Worked on a project to improve the learning algorithm efficiency of speech recognition software using parallel computing.
  • Used MPI and OpenMP libraries in C to incorporate parallel programming features in the software.
CMPIOpenMP

Education

Indian Institute of Technology, Guwahati

Bachelor of Technology (BTech)

Jan 2009Jan 2013

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