Hi đź‘‹, I'm Sai Ganesh N
Welcome to My Homepage
I am an experienced Machine Learning Engineer with a strong background in AI/ML deployment, cloud-based MLOps, and Generative AI solutions. I have built and deployed LLM-powered AI agents at Paramount and RAG-based enterprise query systems at WhyofAI for real-time insights.
My expertise spans AI/ML Deployment, Cloud & MLOps, and Generative AI & NLP. I have hands-on experience with GCP, AWS, Azure, Docker, and CI/CD for scalable model deployment. Additionally, I have optimized NLP pipelines and built GPT-based AI models using TensorFlow, and PyTorch.
My background also includes Computer Vision & Data Science, where I developed image classification models and designed scalable ML workflows using OpenCV and deep learning architectures. Previously, I worked as an AI Engineer (R&D) at Zoho CRM (SaaS), as a Computer Vision (AI) Research Intern at Amrita CREATE + UMANG Indian e-Governance Services, and as an ML Intern at Easycrop.
I have also contributed to machine learning and IoT research during my undergraduate program and served as a Teaching Assistant for the MBAI program at Kellogg. With my expertise in AI-driven automation, cloud ML workflows, and scalable NLP solutions, I am excited about leveraging AI to solve complex challenges.
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A glimpse of the projects I've been working on
OptimAIzer
Resume Boost AI Tool - Optimize Your Resume for ATS and Land Your Dream Job
OptimAIzers transforms your resume into a job-winning masterpiece by combining AI-driven PDF parsing, job description scraping, and tailored analysis—all powered by AWS cloud services for seamless performance. Elevate your profile for ATS success and land your dream role with a few effortless clicks.
InnovateChatbot
Same Same Collective - Chatbot tailored for LGBTQI+ youth in South Africa and Zimbabwe
Same Same Collective Chatbot leverages a fine-tuned LLM and trained BERT model, and Logistic Regression model to accurately classify user emotions and detect suicidal risk. Tailored for LGBTQI+ youth in South Africa and Zimbabwe, it delivers empathetic responses and real-time interventions to provide crucial mental health support. This innovative blend of advanced NLP and compassionate design ensures timely, life-saving engagement.
(Computer) Vision with Lost Glasses
Modelling how the brain deals with noisy input
Imagine you lost your spectacle and the world around you is completely blurred out. As you stumble around, you see a small animal walk towards you. Can you figure out what it is? Probably yes right? In this situation, or in foggy/night-time conditions, visual input is of poor quality; images are blurred and have low contrast and yet our brains manage to recognize it. Is it possible to model the process? Does previous experience help?
Predicting the decision based on BFC
Maintaining the Brain Functional Connectivity (BFC) in the gambling task using the HCP fMRI data-set.
Are there any regions and networks in the brain which are more affected by the Win and Loss Event conditions corresponding to someone’s decision? To analyze data-set being preprocessed by HCP© for NMA-2021 that they recorded using fMRI is to analyze in this project to maintain connectivity and correlation of the areas which are involved in the task.
DeepFakes Generation Using Deep Learning
DeepFakes Generation Techniques for Face Forensics Using Deep Learning DCGAN model
Deep fakes are a machine learning approach in which a person’s resemblance is changed in an existing image or video. Deep fakes have become a social problem since they allow anyone’s image to be co-opted, putting our ability to believe what we see into question. We create a GAN to generate deepfakes in this project.
Time Series Forecasting based on Ethereum Prices
Time Series Forecasting based on Ethereum Cryptocurrency Stock prices data with different Machine Learning and Deep Learning models
A machine learning-based time series analysis was used to anticipate the market price and stability of Ethereum in the Crypto-market. Time-series analysis can forecast future price fluctuations in Ethereum. For time series analysis, we employed LSTM, moving averages, ARIMA, and FBProphet as machine learning approaches.
Super Mario Playing Agent Using RL
Super Mario Playing Agent using Double DQN Reinforcement learning method
In this project, we use the PyTorch library to create the Reinforcement Learning method using the Deep Double Q-Network (DDQN) algorithms. We demonstrate how the recently developed Double Q learning (DQN) technique, which combines Q-learning with a deep neural network, may be utilised to create an agent that assists in completing levels in Super Mario Bros.
Question Answering System
Question Answering System using Natural Language Processing (NLP) models (Splinter and SpanBERT)
In this project we present the use of Splinter and SpanBERT models to showcase and try to solve the question-answering problem both in the closed domain and open domain as well, where the dataset used was (open domain) is SQuAD 2.0 and also a separate dataset (COVID dataset) has been generated by us for the splinter model (closed domain).
Speech recognition with HMM
Speech Word-Recognition with Hidden Markov Model (HMM)
The speech recognition system implemented during this project trains one hidden Markov model for each word that it should be able to recognize. The models are trained with labeled training data, and the classification is performed by passing the features to each model and then selecting the best match.
ChatZ
An Ethereum-powered Decentralized chat application.
With the persisting scenario of increased privacy and security breaches in chatting and social media platforms, there’s been a sense of insecurity stirring amongst people globally. Decentralized applications use peer-to-peer networks, this eliminates the possibility of network failure due to central node failure. Blockchain functions as an immutable ledger that allows for decentralized messaging.
Object Detection using YOLO-V4
Object Detection on Images/Videos/Webcam using Tensorflow-YoloV4
With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. This YOLO-V4 behave differently in network architecture, training strategy and optimization function, etc.
VIRTUAL-TV
Capture a video of a TV frame and inlay a video to fit this frame
In this project the basic aim was to try and fit in the overlay of a subject video onto another video containing the respective frame for playing the latter subject video. The solution to this problem was achieved by making the user define the four corners of the frame and then the previously mentioned corners using the Lucas-Kanade Optical Flow function in OpenCV were being kept track of.
Phone-Directory
Basic Phone Directory using JAVA
Data Structure and Algorithm (DSA)-based project which performs basic Phone Directory function abilities using the concepts of Binary Search Tree and Object-Oriented Programming using JAVA.
Movie Recommendation System with Apache Spark
Created a Movie Recommender System that seeks to predict or filter preferences according to the user’s choices. Used Spark MLlib library for Machine Learning provides a Collaborative Filtering implementation by using Alternating Least Squares.
Image Restoration using Deep Image Prior based on deep CNN’s
Worked on an existing research project called deep image prior which is based on image restoration which solves all image inverse applications with a new deep-prior algorithm.
