• 🎓 B.Tech in Artificial Intelligence & Machine Learning (2022–2026), SIRT, Bhopal
• 🔭 Currently building end-to-end ML & Computer Vision applications — from data pipeline to deployed API
• 🧩 Comfortable across the full stack of a data science project: EDA → feature engineering → modeling → API → deployment
• 🤖 Actively exploring Generative AI, LLMs, and RAG architectures
• 📫 Reach me at sayyedsohelali448@gmail.com
• ⚡ Fun fact: I like turning research papers and messy datasets into working, deployed products

Languages

Data Science & Machine Learning

AI / Deep Learning

Web & Application Development

Databases

Tools & Platforms

Python Mistral AI FastAPI SSE JavaScript  •  🔗 Live Demo

A modular four-stage agentic workflow (Search → Read → Write → Critique) powered by Mistral AI that automates end-to-end research generation.

• Integrated Tavily Search API + BeautifulSoup for intelligent web-scraping and information extraction
• Built a FastAPI backend using Server-Sent Events (SSE) to stream real-time agent execution status to the frontend

Python Pandas Scikit-learn FastAPI  •  🔗 Live Demo

An end-to-end predictive classification system benchmarking XGBoost and ensemble methods for default-risk identification.

• Built a robust preprocessing pipeline: median imputation, Z-score outlier detection, One-Hot encoding
• Tuned models via RandomizedSearchCV, improving F1-score and generalizability
• Deployed as a REST API with FastAPI + Joblib, with interactive Swagger docs

Python Flask OpenCV scikit-image SciPy  •  🔗 Live Demo

An end-to-end computer vision application for microscopy image analysis — cell/nuclei segmentation, quantitative feature extraction, and automated quality control, built on an OpenCV-based classical CV pipeline.

• Full pipeline: thresholding → morphological cleanup → distance transform → watershed segmentation
• Flask REST API + browser dashboard with per-cell CSV export and benchmarking against public datasets

Python Pandas NumPy Scikit-learn SMOTE

A fraud detection framework built to identify anomalous transactions in highly imbalanced financial data.

• Applied SMOTE to correct class imbalance, boosting minority-class recall by 15%
• Validated with Precision-Recall curves and ROC-AUC analysis for reliable fraud detection

B.Tech in Artificial Intelligence & Machine Learning (2022 – 2026) Sagar Institute of Research and Technology (SIRT), Bhopal — 6.93 / 10 CGPA

• Professional Certification in Data Science — Raj Institute of Coding & Robotics (RICR), Bhopal (2026)
• Data Science & Advanced Visualization — SAGE Summer School, SAGE University (Jun–Jul 2025)

Open to Data Science / Machine Learning Engineer roles and collaborations on interesting AI projects.