Technical Lead at KPIT Technologies| M.Tech (EC) from IIT Kharagpur|
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Tech lead in KPIT with experience in Computer Vision, ADAS and AUTOSAR
Indian Institute of Technology, Kharagpur
Master of Technology - MTech, Visual information processing and embedded systems
January 1, 2019 – January 1, 2021
Academy of Technology
Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering
January 1, 2012 – January 1, 2016
KPIT
Technical Lead
April 1, 2023 – Present
KPIT
Computer Vision Engineer
July 1, 2016 – April 1, 2023
Inter communication handler
May 1, 2022 – Present
Skills: Python · Algorithms · Code Generation · NumPy · Pandas · AUTOSAR
Digital Mirror System
June 1, 2021 – May 1, 2022
Skills: C · Deep Learning · Keras · Python · Algorithms · Machine Learning · Artificial Intelligence (AI) · Neural Networks · C++ · OpenCV · Image Processing · TensorFlow · open cv · Scikit-Learn · Computer Vision · Pattern Recognition · C (Programming Language)
Autonomous vehicle
October 1, 2016 – June 1, 2019
Skills: Deep Learning · Keras · Digital Signal Processing · Python · Algorithms · Machine Learning · Artificial Intelligence (AI) · Neural Networks · OpenCV · Image Processing · TensorFlow · open cv · Computer Vision · Pattern Recognition
Voice commandable intelligent wheelchair with smart braking system
September 1, 2015 – February 1, 2016
New engineering developments offer opportunities to develop assistive technology that can improve the lives of many people who use wheelchairs. Our voice recognition system will detect these voice commands and send a signal to the microcontroller. After reading that signal from the voice recognition system microcontroller will process the signal. It will send the output signal to the Motor driver circuit. Motor driver circuit will read output signal from microcontroller and control the motor speed according to the signal. Our aim of this intelligent wheelchair project is also to enhance the smartness of the voice-controlled wheelchair using sensors to perceive the wheelchair's surroundings. For this purpose, we are also going to include some safety features to the system like ultrasonic distance calculation of any obstacles in track. Ultrasonic transducers gives distance measurement precision up to 1mm and a range of 4meters, it continuously checks the roads condition (Example: if there are any stair which goes downward). These features will help the microcontroller to take the right decision. If a voice-command comes from the user then microcontroller will check if the command can be performed in reality. If yes, then only the microcontroller will give the motor driver the processed instruction to perform the command. For an example, If a person wants to turn right but there is a wall present very close to right side then wheelchair won’t turn right using the designed distance calculator. Smart braking is made by ultrasonic distance calculators which calculates the distance between obstacle and the wheel chair by using that data it gradually decreases the speed of the wheelchair before stop. It gives extra comfort to the user we can achieve higher speed and safer breaking system. User can monitor space clearance in all directions.
Distance calculator using ultrasonic waves
December 1, 2014 – February 1, 2015
Skills: Robotics · Eagle PCB · Arduino
Cultural Fit Analysis
The candidate's project experience is heavily focused on embedded systems, computer vision, and autonomous vehicles, which, while technically strong, may not directly align with the typical breadth of data analysis roles that often involve business intelligence, statistical modeling, or diverse data sources beyond sensor/image data. The projects are primarily personal, limiting insight into team collaboration or broader industry application. The target role is 'Data Analyst', but the candidate's experience leans more towards 'Data Scientist' or 'Machine Learning Engineer' with a specialization in computer vision. This could indicate a potential mismatch in expectations or a need for the candidate to broaden their data analysis skill set.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit. The psychometric test score is 0, indicating no data was provided for this assessment.