AI is analyzing your overall score…
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI | Perception | Machine Automation | Deep Learning | Perception Simulation | GenAI
Computer vision, Deep learning , Artificial Intelligence and Autonomous vehicle enthusiast. Area Worked in • Deep domain expertise in applying machine learning, computer vision and computer graphics to push simulation realism. • Image Segmentation using Deep learning techniques. • Algorithm development for lane tracking using opencv. • Deep learning based Traffic sign classificataion. • Behavioral Training of car for Autonomus driving based on CNN using TensorFlow and Keras. • Lane identification and calculation of the curvature of the road along with the offset calculation of the vehicle from the center of the lane using openCV. • Vehicle detection using machine learning techniques. • Design and Development of mini Unmanned Aerial System for ISR platforms. • Helmet Mount head Tracking and Display system. • Cockpit Displays based on Arinc661, HUD based on Mil-1787B symbology standards • Navigation systems, Digitial Moving Map, Electronic Flight Bag(EFB) based on Arinc 424-18 • PID control loops, Kalman Filter, Extended Kalman Filter, Uncented Kalman Filter and Sensor fusion Algorithm development for MEMS based sensors Specialties: C, C++, Arduino, Python, VC++, Eclipse, Qt, LabWindows CVI \ LabVIEW, MatLab \ Simulink, OpenGL, OpenCV, Tensorflow, Keras, ROS MS-Office, X-Plane, JSBSim, FlightGear, SVN & Doors Protocol : Mil-Std-1553B, ARINC 429, RS232, RS422\485, LXI, GPIB, I2C, CAN , SPI Standards : Arinc661, Arinc424-18, Misra c/c++, DO178B/C. Certified in Aerial Robotics from University of Pennsylvania My github Link: https://github.com/gkbell46
Birla Institute of Technology and Science, Pilani
Master of Technology - MTech, Data Science Engineering
October 1, 2021 – October 1, 2023
Udacity
Self Driving Car Nano Degree, Deep neural networks, Artificial intelligence, Machine learning
January 1, 2016 – January 1, 2017
University of Pennsylvania
Certification, Aerial Robotics
January 1, 2016 – January 1, 2016
Visvesvaraya Technological University
Engineering, Electronics and Telecommunication
January 1, 2008 – January 1, 2012
Caltech
Systems Engineering Fundamental, Systems Engineering
N/A – Present
John Deere
CVML Engineer - Synthetic Data
May 1, 2025 – Present
John Deere
Senior Lead Engineer - Machine learning Simulation
July 1, 2022 – April 1, 2025
John Deere
Lead Engineer - Intelligent Solutions Group
December 1, 2018 – August 1, 2022
KritiKal Solutions
Lead Software Engineer - CVIP Team
November 1, 2017 – November 1, 2018
New Delhi Area, India
Blinkware Technology
Head Of Research And Development
July 1, 2017 – October 1, 2017
Petaling Jaya, Selangor, Malaysia
Udacity
Mentor
April 1, 2017 – September 1, 2018
Atlas Copco
Senior Software Developer
August 1, 2016 – July 1, 2017
Bengaluru Area, India
Dynamatic Technologies Limited
Software Development Manager
September 1, 2015 – August 1, 2016
Bengaluru, Karnataka, India
Aviohelitronics Infosystems Pvt Ltd
Software Developer
February 1, 2013 – August 1, 2015
Bengaluru, Karnataka, India
Avio Helitronics Info systems
Hardware Design Engineer
August 1, 2012 – January 1, 2013
Bengaluru, Karnataka, India
Electronics and radar development establishment(LRDE),DRDO
Project Trainee
September 1, 2011 – April 1, 2012
C V Raman Nagar Bengaluru, Karnataka 560093
Segmentation of White blood cells (WBC) from blood using U-NET
June 1, 2017 – Present
The problem gives solution to segment White blood cells (WBC) from the blood using Deep learning techniques. The model uses U-Net architecture with very less number of training data.
Extended Kalman Filter Project
April 1, 2017 – Present
Implemented Extended Kalman Filter for RADAR and LIDAR sensor data.
Advanced Lane Line detection
March 1, 2017 – Present
Project Involved: * Computing the camera calibration matrix and distortion coefficients given a set of chessboard images. * Applying distortion correction to raw images. * Use of color transforms, gradients, etc., to create a thresholded binary image. * Applying perspective transform to rectify binary image ("birds-eye view"). * Detecting lane pixels and lane fitting to find the lane boundary. * Determining the curvature of the lane and vehicle position with respect to center. * Warping the detected lane boundaries back onto the original image. * Outputting visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Vehicle Detection
February 1, 2017 – Present
The developed pipeline tracks the cars detected along the lanes. Canny edge detection, Hough transformation and Histogram of oriented gradients (HOGs) were used for tracking. It is capable of detecting multiple cars from single frame.
Behavioral Training of Car
January 1, 2017 – Present
This project is all about capturing the human subcongnitive driving skills and reproduce them using convolutional neural network model. The simulator developed by the Udacity was used. Data for training was collected by driving the car around the track. Images from center, left and right camera along with the corresponding steering angles were used as training data .Neural network model was developed and the was trained using the collected data. The trained convolution neural network was used to drive the car around the track in autonomous mode.
Traffic Sign Classifier
January 1, 2017 – Present
The project deep neural networks and convolutional neural networks to classify traffic signs. Involved training a model so it can decode traffic signs from natural images by using the German Traffic Sign Dataset. After the model is trained, model is tested using model program on new images of traffic signs.
Lane Lines Detection
December 1, 2016 – Present
The project involves development of image processing algorithm to detect lane lines on the road using Canny edge detection and hough transforms. Algorithm was developed in python and uses OpenCV libraries.
Building AI Agents with Multimodal Models
NVIDIA
June 24, 2026 – Present
Structuring Machine Learning Projects
DeepLearning.AI
June 24, 2026 – Present
Generative Adversarial Networks (GANs) Specialization
DeepLearning.AI
June 24, 2026 – Present
Master AI Image Generation using Generative AI
Udemy
June 24, 2026 – Present
Autonomous cars: Deep Learning and Computer vision in python
Udemy
June 24, 2026 – Present
Certified Scrum Product Owner® (CSPO®)
Scrum Alliance
June 24, 2026 – Present
Robotics: Aerial Robotics (University of Pennsylvania)
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is heavily skewed towards Computer Vision, Machine Learning, and Autonomous Systems, which aligns well with roles requiring deep analytical and model development skills. However, the target role is 'Data Analyst', which typically involves a broader scope of data manipulation, statistical analysis, and business intelligence, beyond just CV/ML. While the candidate possesses strong technical depth, the direct alignment with a general 'Data Analyst' role might require a shift in focus or a role specifically tailored to their CV/ML expertise. The diversity of projects within their specialized domain is good, but the breadth across different data analysis techniques (e.g., SQL, traditional BI tools, A/B testing) is not explicitly demonstrated.
Soft Skills & Operational Fit
The candidate's experience as a Lead Engineer and Mentor suggests strong communication, problem-solving, and leadership skills. Involvement in Agile Methodologies indicates adaptability and a structured approach to project execution. The descriptions of project work, while not explicitly detailing soft skills, imply a capacity for independent work and collaboration within technical teams.