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MasterCard
Software Engineer
June 15, 2026 – Present
Seismic-Data-Handling
October 8, 2020 – March 13, 2021
Generating Synthetic Seismogram from petrophysical well logs and geological interpretation data
View ProjectVignette-Filter-using-OpenCV
September 5, 2020 – September 5, 2020
Creating a Vignette Filter using OpenCV in Python
View ProjectDocument-field-detection-using-Template-Matching
September 5, 2020 – September 5, 2020
Document field detection using Template Matching incorporating the OpenCV library in Python
View ProjectCreate-an-Air-Canvas-using-OpenCV-
September 5, 2020 – September 5, 2020
Creating an air canvas using Opencv Python
View ProjectDepth-Map-from-Stereo-Images
September 5, 2020 – September 5, 2020
Creating a depth map from stereo images using computer vision
View ProjectSobel_edge_detection
July 7, 2020 – July 10, 2020
Sobel_edge_detection — GitHub repository
View ProjectNeural-Style-Transfer-Tensorflow
May 17, 2020 – May 17, 2020
Neural style transfer using tensorflow
View ProjectASR-for-Hindi
February 26, 2020 – April 9, 2020
Creating an Automatic Speech Recognition Pipeline for Hindi using the Kaldi toolkit.
View ProjectCultural Fit Analysis
The candidate's projects are predominantly in AI/ML, computer vision, and data processing, which aligns with a specialized technical role rather than a general 'Software Engineer' role without further context. The projects are all personal, indicating initiative but lacking evidence of collaborative team environments. The listed experience at MasterCard as a 'Software Engineer' is current but lacks details on responsibilities or skills, making it difficult to assess alignment with typical software engineering practices in a corporate setting. The breadth of skills is good, but the depth in core software engineering principles (e.g., data structures, algorithms, distributed systems) is not explicitly demonstrated.
Soft Skills & Operational Fit
The candidate's project descriptions are concise, indicating a direct communication style. The variety of personal projects suggests self-motivation and a proactive approach to learning and applying new technologies. However, without specific psychometric test results, it's difficult to assess logical reasoning, work attitude, stress handling, or team collaboration.