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
Analyst (Machine Learning and Data Team)
Deloitte USI
- Conducted internal research to develop and implement machine learning solutions, optimizing processes and addressing business challenges using Python, TensorFlow, and scikit-learn.
- Extracted, cleaned, and prepared large datasets, ensuring data quality and consistency for predictive analytics.
- Performed exploratory data analysis (EDA) and crafted insightful visualizations with matplotlib, seaborn, and Tableau to provide actionable insights for internal stakeholders.
- Partnered with cross-functional teams to design and deploy machine learning models into internal systems, leading to significant operational improvements and innovation-driven outcomes.
Jun 2021 – Jun 2022
Data Engineering Intern
Lead Squared
- Engineered scalable ETL pipelines to preprocess and transform large datasets from relational databases (MySQL, PostgreSQL), ensuring seamless integration with business analytics systems and meeting performance benchmarks.
- Designed innovative data workflows to deliver high-quality, preprocessed datasets for training machine learning models, significantly reducing training times and improving model accuracy through optimized data handling.
- Conducted advanced data preparation and exploration using pandas and SQL, and developed compelling visualizations with matplotlib, seaborn, and Tableau, enabling leadership to make data-driven decisions.
- Collaborated with cross-functional teams to integrate robust data pipelines with machine learning models, driving advanced customer segmentation and enabling personalized marketing strategies.
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.