Results-driven ML Engineer and Data Scientist with a Master's in Data Science and over 4 years of experience in Machine Learning, Generative AI, and Cloud Technologies. Proficient in leveraging advanced ML models, including CNNs and RAG pipelines, to deliver cutting-edge solutions in industries like healthcare and financial technology. Expertise in cloud platforms, data visualization, and full-cycle project management, enabling seamless deployment and strategic innovation. Known for collaborative problem-solving, technical excellence, and a strong passion for driving business success through scalable AI-powered solutions.
Focused on end-to-end ML model deployment, leveraging MLOps, CI/CD, and cloud solutions to deliver scalable and production-ready AI systems. Experienced in optimizing workflows, enhancing decision-making with actionable insights, and managing the full model lifecycle. A technical innovator dedicated to creating impactful, AI-driven solutions.
A modular end-to-end ML pipeline that predicts wine quality from chemical properties using advanced regression models.
An end-to-end ML pipeline built with modular MLOps practices, ensuring scalability, reproducibility, and experiment tracking using MLflow and cloud integrations.
Using Langchain to build LLM Agent that can work with RAG and Wiki Tool (For Live Info) understanding the user Command.
Built using RAG using Mistral Open Source LLM and LangChain on Streamlit. Deployed it on Huggingface.
Built a personal portfolio website from scratch using Flask, Docker, Github Actions and AWS EC2
Tool designed to integrate and analyze transactional data, creating visualizations and reports.
Comprehensive data cleaning and exploratory analysis of a layoffs dataset to uncover trends, patterns, and company-specific insights over time.
End-to-end deep learning project for chicken disease classification using MLOps DVC pipeline, deployed on AWS.
Detecting fraudulent credit card transactions using data preprocessing, exploratory analysis, and machine learning classification techniques.
A mini flask app CI/CD deployment with github actions and Docker image generation.
Web-based platform designed to allow users to securely upload, manage, and view images
The objective of the project is to develop a deep learning system for detecting Melanoma skin cancer
Using QR codes to manage equipment and update the status of previous repairs and managing the access by user authentication.
| Data Analysis | Cleaning, Manipulation, EDA, Pattern Extraction, Data Mining |
| Statistical Analysis | Hypothesis Testing, Regression, A/B Testing |
| Data Visualization | Tableau, Power BI, Matplotlib, Seaborn |
| Programming | Python, C, C++, Java, R, Flask, Fast API, Django |
| Database Management | SQL, MySQL, MongoDB |
| Cloud Platforms | Google Cloud Platform, AWS, Kubernetes |
| Machine Learning | Decision Trees, Random Forest, Logistic Regression, Clustering, NLP, RASA |
| Deep Learning | Neural Networks, CNNs, RNNs, GANs, Tensorflow, Keras |
| Big Data | Hadoop, Spark, Pyspark |
| Tools | MLOps, MLFlow, Git, Github, Github Actions, Excel, Google Analytics, Apache Airflow |
| Gen AI | RAG models, LLMs, Agents, Llama index, LangChain, Vector DB, Embeddings, LoRa & QLoRa fine tuning methods, Prompt Engineering |