Hi, I'm Shohaib. I build scalable, reliable systems for people, not just spec sheets.
Fullstack engineer with a Master's in CS from Boston University. I turn messy real-world problems into clean, working software across React, Node, Python, and AWS.
Software that actually helps the people using it.
I'm a passionate and detail-oriented Fullstack Software Engineer with a strong foundation in computer science and hands-on experience in full-stack development, automation, and cloud-based systems. With a Master's degree in Computer Science and real-world experience across startups and internships, I enjoy solving complex problems through clean, efficient code and collaborative teamwork.
Two schools, two continents.
Boston University
Focus: Artificial Intelligence, Machine Learning, Cloud Computing, and Distributed Systems.
Savitribai Phule Pune University
Capstone: E-Health Patient Management System using voice recognition + ML to predict diseases from symptoms.
Real work, real outcomes.
Aaum AI
- —Developed and integrated scalable APIs in Node.js (TypeScript), implementing Razorpay payment processor with webhooks for a subscription-based model.
- —Built an agentic RAG service (Python · FastAPI · LanceDB · Agno) so the product can actually read the web and remember what it learned.
- —Integrated multiple LLM providers (OpenAI, Perplexity, and Grok) for dynamic chat completions, enhancing response diversity and reliability.
- —Built responsive front-end interfaces with React (TypeScript) and Tailwind CSS, leveraging Zustand for efficient state management.
- —Deployed services using Docker on AWS EC2, ensuring scalable, fault-tolerant infrastructure.
- —Designed REST + GraphQL endpoints for a microservices backend aligned with platform reliability goals.
- —Stitched Postgres, MongoDB, and Redis together with proper failovers and integrated fault-tolerant data layers.
- —Set up real-time monitoring with OpenTelemetry + Grafana Loki and outages stopped being a guessing game.
- —Shipped OAuth2 / JWT auth and a GitLab CI/CD pipeline the team actually trusts.
Two papers I'm still proud of.
An E-Health Patient Management System.
Patients describe symptoms by voice; the system transcribes them, extracts medical terms with NLP, and predicts disease with a Random Forest model at 93.55% accuracy across 41 diseases.
Read paper →Proposed Model of Speech Recognition using MFCC and DNN
A speech-to-text pipeline combining MFCC feature extraction with deep neural networks built for noisy, large-scale datasets where SVMs start to fall apart.
Read paper →Things I built because I wanted to.
Travel Genie
A travel planning assistant that uses AI to provide personalized recommendations based on user preferences and travel history.
Fynd Me
A shelf recognition system that uses computer vision and machine learning to identify and recognize shelves in images.
Hey Gen
A utility library that implements a polling mechanism to check the status of asynchronous jobs.
Trade Pulse
TradePulse uses advanced machine learning to analyze stock trends and predict market movements. Get real-time insights, risk assessments, and custom alerts to make informed trading decisions.
Hiring? Let's talk.
The fastest way to know if I'm a fit is a quick email or a 15-minute call. No prep, no slides, just a chat.