Senior Technical Lead (ML/DL) at Mercedes-Benz R&D | IIT Delhi | Ex-Bosch
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Currently working as a Senior Technical Lead at Mercedes‑Benz R&D India with total 6.5+ years experience specializing in developing machine learning and deep learning models. Demonstrated experience in building models from scratch for automotive business use‑cases and deploying it on edge devices. My research work is also published in top computer vision conferences such as CVPR, ECCV. Specialties - Python, Pytorch, Data Science, Machine learning, Deep learning
Indian Institute of Technology, Delhi
Master of Science - MS, Mathematics and Statistics
January 1, 2014 – January 1, 2016
Panjab University
Bachelor of Science - BS, Mathematics and Computer Science
January 1, 2011 – January 1, 2014
Mercedes-Benz Research and Development India
Senior Technical Lead
April 1, 2023 – Present
Mercedes-Benz Research and Development India
Senior Research Engineer
April 1, 2021 – March 1, 2023
Mercedes-Benz Research and Development India
Research Engineer
April 1, 2018 – March 1, 2021
Robert Bosch Engineering and Business Solutions india
Machine Learning Engineer
August 1, 2016 – March 1, 2018
Bengaluru, Karnataka, India
Generative adversarial networks to generate new images of faces.
May 1, 2017 – Present
Generative adversarial networks to generate new images of faces was made using a discriminator and generator using convolution layers along with batch normalization in tensorflow. This was done for black and white images on MNIST dataset as well as colored images with CelebA dataset.
TV script Generation using LSTM
March 1, 2017 – Present
Apply LSTM model to generate new TV Scripts for The Simpsons TV series for a particular episode. SImple RNN network with a single layer LSTM was used to train the network and the model parameters were used to generate new text based on the already generated text. Skills used: Python, tensorflow, LSTM
Word Embeddings using Deep Neural Networks
March 1, 2017 – Present
Create word embedding’s for a particular data using a single layer neural network to create an embedding vector for each word in the vocabulary. Data is fed in batches after preprocessing to a single layer neural network using a technique called skip gram to generate 300 size word embedding vectors. Data set used was text8 dataset from wikipedia text. Skills used: Python, word2vec, tensorflow, DNN
Language Translation using RNN
March 1, 2017 – Present
Language translation from English to French is done using two LSTM layers using tensorflow library in python. Achieved an accuracy of 95% on the validation data. Skills Used: Python, Deep learning, tensorflow, LSTM
Style Transfer on images
February 1, 2017 – Present
Transfer the style of one image onto the content image using pre trained VGG net. Optimization of the losses were done using LBFGS method.
Image Classification using CNN
January 1, 2017 – Present
Multiclass Classification done to classify images with 10 different objects using Convolutional Neural Network using tensorflow on CIFAR-10 dataset.
Stochastic Modelling of Cervical Cancer in India
April 1, 2016 – Present
Aim of the project : It was based on stochastic analysis of cervical cancer in india with efforts for detection, cure and cost reduction of cancer at early stages with minimum transition between the different states(cancer stages). Skills Used: Statistical Modelling, Markov Process, Linear Algebra , Probability Theory , Differential Equations , Methods Of Applied Mathematics , Statistical Inference , Numerical Analysis
Deep learning Udacity Nanodegree
Udacity
June 24, 2026 – Present
Introduction to Programming In C
Indian Institute of Technology, Kanpur
June 24, 2026 – Present
Programming with Python for Data Science
Microsoft
June 24, 2026 – Present
Statistics Foundations: 2
June 24, 2026 – Present
Machine Learning Foundations: A Case Study Approach
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is heavily focused on deep learning and machine learning algorithms, which aligns well with an ML Engineer role. The experience at Mercedes-Benz R&D and Robert Bosch suggests exposure to corporate R&D environments. However, the projects are primarily personal and academic, and there is no explicit information on collaboration or team-based work within these projects. The breadth of skills is concentrated within ML/DL, which is good for the target role, but might lack diversity in broader software engineering practices or MLOps. The absence of completed psychometric tests limits the assessment of cultural fit.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a focus on technical execution. Without psychometric test results or interview data, it is difficult to assess soft skills such as teamwork, communication, or stress handling. The operational fit for a senior ML Engineer role appears strong given the technical depth in ML/DL.