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Machine Learning Developer at SAP SE
I am currently working as a machine learning developer in SAP SE at Walldorf, Germany. During my nearly 4 year of IT experience, I have worked on both frontend and backend technologies for a large scale mobile product. I love problem solving and posses deep passion of working on smart systems and harnessing them to make day to day life more and more easier. I have excellent interpersonal and organisational skills complemented with an ability to build relationships quickly. Specialties: Java, JavaScript, UI Technologies, Software Development
RPTU Kaiserslautern-Landau
M.Sc. , Computer Science
January 1, 2015 – January 1, 2017
University Institute of Engineering & Technology, Kurukshetra University
Bachelor of Technology (B.Tech.), Computer Science
January 1, 2007 – January 1, 2011
SAP
Machine Learning Developer
April 1, 2017 – Present
Heidelberg, Baden-Württemberg, Germany
Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
Research Assistant
October 1, 2015 – March 1, 2017
Kaiserslautern, Germany · On-site
Amadeus
Senior Software Engineer
June 1, 2012 – February 1, 2015
Bangalore
Ness Technologies (with Amadeus as a development center)
Software Engineer
July 1, 2011 – June 1, 2012
Bangalore
Task Execution Manager for Smart Society
January 1, 2016 – Present
The project aims at developing smart sharing systems to achieve a dream of smart society. It is a distributed project with teams situated around the globe. My major contribution was to design and develop a middle ware monitoring system which is responsible for the handling and monitoring the resource allocation in a smart share system. It involved exposing APIs in NodeJS and using mongoDB database for the data storage.
Person Identification using GAIT cycle
October 1, 2015 – Present
The primary task is to work on an activity person identification algorithm which intelligently recognizes the person based on its GAIT cycle. The data collected from the walking pattern of the person is processed and manipulated to find the useful identifiers which ultimately classifies person using supervised learning and various classifiers. The machine learning algorithm uses openCV with C++ for better image recognition tasks and Qt for UI components. Currently, working on this topic as my master thesis and applying deep learning techniques using transfer learning and domain adaptation
Trending topic discovery and visualization
October 1, 2015 – Present
The project involves discover the trending topics from various social channels and visualize the trends on a web based application. The visualization needs to be adaptive of the nature of the data being analysed which can facilitate the prediction of future trends. As part of this project, built a generic visualization browser using D3.js and Javascript, which also has a generic world globe visualization showing various geolocations.
MeRCI
June 1, 2012 – Present
MeRCI is an Airline ecommerce Mobile Product enabling Airlines to offer Mobile apps and site to it's end-users. It allows airline end users to Search and book on the fly, get regular updates on latest offers and discounts and also Redeem Miles on the fly.
Beischeinigung B1
VKB e.V. TU Kaiserslautern
June 24, 2026 – Present
Oracle Certified Professional Java SE 6 Programmer
Oracle
June 24, 2026 – Present
Goethe Zertifikat A2
Goethe-Institut e.V.
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse background spanning mobile web development, machine learning, and data visualization. While the target role is 'Frontend Developer', a significant portion of recent experience is in Machine Learning. The projects 'Trending topic discovery and visualization' and 'MeRCI' align well with frontend development, showcasing experience with D3.js, JavaScript, and mobile UI. The 'Task Execution Manager' project also demonstrates backend API exposure with NodeJS, which can be beneficial for a frontend role requiring API interaction. The breadth of experience suggests adaptability, but the recent shift towards ML might indicate a different career trajectory than a pure frontend focus. The lack of specific company culture data limits a deeper cultural fit analysis.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an ability to work on complex, distributed projects (Task Execution Manager for Smart Society) and a focus on user experience (MeRCI, Trending topic discovery). The role as a Research Assistant at DFKI indicates an aptitude for problem-solving and continuous learning. However, without psychometric test results, a definitive assessment of soft skills and operational fit is limited.