6 years of experience in building and managing AI Systems.
* I develop end-to-end AI systems from requirements analysis, data gathering to deployment, implementing new methods/research papers, turning projects into research outcomes. Achieved substantial performance in DL/ML models for CV and NLP domain problems.
* Implementing and deploying projects, research papers as well as POCs.
* Project planning, requirements gathering, analysing requirements to define the architecture of the system, and timeline to implement them.
• Machine Learning and Data Visualization: tensorflow, PyTorch, scikit-learn, keras, NumPy, Pandas,
• Programming Methodologies: OOPS, Functional
• Machine Learning Deployment: Docker, Kubernetes
• Software and Frameworks: Flask, Angular, Jupyter, GitHub, git
I like to work on technology that is smart, simple and sophisticated. These sums up the vast amount of knowledge required to work on projects to excel it into a working product. I like to train Deep Neural Networks, and understand them well.
I have mentored many students for their career in AI, taught them Machine Learning and Mathematics. Currently, I am a mentor for the RFS (Reach for the Stars) Programme by Aga Khan Education Board for India. I am an alumnus of this program as well.
I have a cumulative experience of 6 years working in the product and service-based industry for creating Machine Learning projects.
I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working in.
Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, Algorithms, including models in the production environment, deploying ML models.
I have 6 years of experience and have successfully deployed models in the following domains of Deep Learning:
# Object Detection
# Object Recognition
# Image Classification
# Clustering and Annotation
# Image Segmentation
# Medical Image Analysis
# Automatic Segmentation
# Generative Adversarial Networks
# Hidden Markov Models
# Bayes Method
# Variational Autoencoders
# Word Embeddings
# Image Embeddings
# Manifold Learning
# Adversarial Attacks in Federated Learning setting
# Combating Adversarial Attacks in Federated Learning
# Implementing Federating Learning algorithms
# BERT (and its variants)
# GPT (DistilGPT)
# LSTM, GRU, RNN (CNN + RNN)
# Implementing and fine-tuning the above models.
# Creating custom Deep Learning models for the custom use-case
# OpenAI API, langchain, Llama.
# Running LLMs on a custom server (keeping your data private)
# Prompt Engineering
# MFCC representation and classification
Google Cloud Platform
Deep Learning Modeling
Natural Language Processing