Jul 2015 – Feb 2020 · 4 yrs 7 mos · Bengaluru Area, India

• McAfee Agent [Client Location Project]
• Roles:
• Worked on resolving the bugs and maintaining code quality.
• Worked on integrating the existing code base for all supported operating systems to have a common build server. Currently, each operating system has a dedicated code base and a separate build server.
• Language/Tools: C++ 11, GIT.
• Content Collector for Emails, File System and SharePoint
• Roles:
• Determined requirements, designed and developed software APIs using C++ 11 and Python 3.
• Wrote APIs in C++ which expose Exchange MAPI API’s, this makes our application email-aware and able to communicate with Exchange servers, finally lets us collect and modify objects from the exchange server itself.
• Integrated product with Office-365 architecture. Developed local APIs using Python which consumes Microsoft Graph APIs and enables them to access various Microsoft cloud service resources.
• Performed testing and developed automated tests used for volume and latency stress testing, also documented detailed project modules development cycle phases.
• Worked on new modules as well as providing technical feedback for existing modules, locating and devising solutions for bugs and maintaining code quality.
• Mentored new team members.
• Language/Tools: C++ 11, Python 3, Microsoft Exchange – MAPI APIs, Microsoft Graph, Django, Visual Studio 12, SVN.
• Persistent Systems – Internal Products.
• Roles:
• Integrated organization Microsoft Exchange server data with Employee Portal (Pi), which shows employees’ emails and calendar entries on Employee Dashboard.
• Worked on Face Recognition pilot project for Employee Attendance Transaction and integrated with existing Employee data.
• Language/Tools: Python 3, OpenCV, Microsoft Graph, Django.

Jul 2014 – Jun 2015 · 11 mos · Mumbai Area, India

• THESIS - Word Association technique to improve textual search performance.
• Roles:
• Analyzed Wikipedia corpus and developed an algorithm to get an association between words on Wikipedia word corpus.
• Achieved enhanced textual search performance using new approaches based on the concept of word association on word's lexical co-occurrences.
• Achieved enhanced semantic sentence similarity which was to determine whether the message conveyed by one sentence matches the meaning of another sentence.
• Domain: Natural Language Processing, Data Analytics.
• Language/Tools: Python, Natural Language Toolkit [NLTK], SVN.