Open to opportunities

A H Riad

@ahriad

Final-year CSE student pursuing machine learning internships—building deployed AI apps across NLP, feature engineering, and evaluation.

Bangladesh

Message

What I'm looking for

I’m looking for a hands-on Machine Learning internship where I can build and evaluate NLP/deep-learning models, collaborate with engineers, and ship AI-driven products using real data and clean engineering practices.

I’m a final-year Computer Science and Engineering student at Jamalpur Science and Technology University, and I’m actively seeking Machine Learning internship opportunities. I bring a strong foundation in machine learning, NLP, feature engineering, and model evaluation, with experience building deployed AI-driven applications.

In my projects, I designed and deployed a content-based movie recommender using Python, Pandas, Scikit-learn, and feature engineering on movie metadata—using cosine similarity and model evaluation for personalized recommendations. I also built an interactive Olympics analytics dashboard with Pandas, Plotly, and Streamlit to support large-scale data exploration, trend analysis, and country-level performance visualization.

More recently, I developed QuickAI, an AI-powered productivity platform integrating LLM workflows, document interaction, and intelligent utilities with production-ready deployment. I’ve also built full-stack applications like a recruitment platform (JavaScript, React, Next.js, Node.js, Express.js, and structured database design) and a responsive social media app, so I’m comfortable bridging ML/AI capabilities with solid engineering practices.

Experience

Work history, roles, and key accomplishments

Deployed Movie Recommender

Movie Recommender

Designed and deployed a content-based movie recommendation system using feature engineering on movie metadata and cosine similarity. Evaluated model performance to deliver personalized recommendations.

Python Pandas scikit learn Cosine Similarity Model Evaluation Recommendation Systems Git

Joblyzer Recruitment Platform

Joblyzer

Built a full stack recruitment platform with scalable backend integration and structured job workflows. Implemented modern database design to support efficient job lifecycle operations.

Full Stack TypeScript Database Design Backend Integrations MongoDB Git

QuickAI AI Productivity Platform

QuickAI

Developed an AI-powered productivity platform that integrates LLM workflows and supports document interaction. Deployed the solution in a production-ready setup.

Document Interaction Generative AI NLP Neural Networks Python Git

Converso Social Media App

Converso

Created a social media application enabling user interaction and content sharing. Implemented responsive frontend architecture for a smooth user experience across devices.

React Next.js TypeScript Frontend Development Responsive Design Javascript (ES6)Git

Olympics Data Analysis Dashboard

Olympics Data Analysis

Built an interactive analytics dashboard for large-scale Olympics data exploration, trend analysis, and country-level performance visualization. Used data processing and visualization tooling to support fast insight discovery.

Pandas Plotly Streamlit Data Visualization Data Analysis Trend Analysis Statistics Git

Education

Degrees, certifications, and relevant coursework

Jamalpur Science and Technology University

Bachelor of Science in Computer Science and Engineering, Computer Science and Engineering

Grade: CGPA 3.63/4.00

Activities and societies: Relevant coursework: DSA, Computer Networks, Statistics & Probability, OOP, Software Engineering, Theory of Computation, DBMS, Artificial Intelligence, Operating Systems, Machine Learning. Projects: Movie Recommender (Python/Pandas/Scikit-learn), Olympics Data Dashboard (Pandas/Plotly/Streamlit), QuickAI (LLM workflows), Joblyzer (full-stack recruitment platform), Converso (social media app).

Final-year Bachelor of Science in Computer Science and Engineering student with CGPA 3.63/4.00. Coursework and projects emphasize machine learning, NLP, feature engineering, model evaluation, and full-stack development.