About
Hey, I’m Sri Ganesh Vathumalli, a Machine Learning Engineer & Data Scientist with a passion for building smart, scalable AI solutions. I have a strong background in AI, NLP, and Deep Learning, along with hands-on experience in Python, PyTorch, TensorFlow, and cloud technologies like AWS and Azure ML.
I come from a Computer Science & AI background, where I developed a solid understanding of Data Structures, Algorithms, and Model Architectures. Alongside AI, I’ve worked with Data Visualization, Forecasting, and Business Intelligence tools, turning raw data into meaningful insights.
Outside of work, you’ll probably find me gaming, exploring new tech, or solving coding challenges just for fun. I enjoy connecting with like-minded people, discussing AI trends, and working on cool projects. If you’re into AI, tech, or just want to chat, let’s connect!
Skills
Programming
AI Frameworks
Web Technologies
Databases
Cloud & DevOps
Tools
Work Experience
Jun 2022 – Jul 2023
Machine Learning Engineer
Deloitte USI
- Developed and deployed end-to-end machine learning models using Python, TensorFlow, and scikit-learn to automate internal processes and solve domain-specific business challenges.
- Extracted, cleaned, and transformed large-scale structured datasets to support predictive analytics, ensuring high data integrity and consistency across pipelines.
- Performed exploratory data analysis (EDA) and created visualizations with matplotlib, seaborn, and Tableau to uncover trends and present insights to internal stakeholders.
- Collaborated with engineering, product, and analytics teams to integrate ML models into internal systems, improving key performance metrics and enabling innovation-driven decisions.
Jun 2021 – Jun 2022
ML Engineer Intern
LeadSquared
- Contributed to full-lifecycle ML projects by building scalable ETL pipelines to process training data from relational databases like MySQL and PostgreSQL.
- Engineered and optimized feature pipelines to improve model training speed and accuracy, supporting various personalization and targeting use cases.
- Utilized pandas, SQL, and Tableau to analyze large datasets and generate visual reports for internal use in customer behavior modeling.
- Worked with cross-functional teams to integrate ML models into production systems, enabling real-time customer segmentation and personalized marketing campaigns.
Featured Projects
Neural Style Transfer Web App
- Designed and developed an interactive web-based Neural Style Transfer (NST) application using TensorFlow and Streamlit.
- Implemented an efficient NST model to transform input images into artistic representations using pre-trained CNN models.
- Integrated Google Colab for model training and hosted the app on Hugging Face Spaces for public accessibility.
Sentiment Analysis on Customer Reviews
- Developed a sentiment analysis model to classify customer reviews as positive, negative, or neutral.
- Fine-tuned the DistilBERT transformer model on a custom dataset to improve accuracy.
- Deployed a FastAPI-based backend with a Streamlit dashboard to visualize sentiment trends in real time.
Healthcare Chatbot using Falcon-1B
- Built a domain-specific healthcare chatbot fine-tuned on medical question-answer datasets.
- Applied LoRA for parameter-efficient fine-tuning of Falcon-1B, optimizing it for medical-related inquiries.
- Deployed the chatbot on Hugging Face Spaces with a user-friendly interface using Streamlit and FastAPI.