Hello World, my name is

Muneeb Ur Rehman Siddiqui.|

I am an Artificial Intelligence Engineer with hands-on experience building and deploying end-to-end AI applications across Computer Vision, Natural Language Processing, and Retrieval-Augmented Generation (RAG).

Leveraging a strong foundation in designing data pipelines and fine-tuning machine learning frameworks, I also focus on developing production-oriented systems using tools like FastAPI and Docker to integrate intelligent solutions directly into user-facing applications.

Fully committed to building systems that bridge the gap between complex models and real-world utility.

Capstone Projects

Download my latest CV

01. About me

Hello, I'm an Artificial Intelligence Engineer based in Karachi, Pakistan.

I've always been fascinated by complex systems, whether they exist in technology, nature, or the fictional worlds. The stories that resonate with me most are those that leave no stone unturned, where every detail, connection, and consequence matters.

That same curiosity is what drew me toward Artificial Intelligence. I enjoy exploring how data, algorithms, and software come together to create systems that are greater than the sum of their parts. Rather than simply using existing tools, I'm most interested in understanding how they work and how they can be pushed further.

I am particularly drawn to projects that involve experimentation, research, and discovering new approaches to difficult problems. Whether it's Computer Vision, Data Science, or emerging developments in AI, I enjoy learning how ideas evolve and where the field is heading next.

I remain optimistic about the future of AI and the opportunities it creates, believing that today's research can become tomorrow's everyday reality.

Hard Skills:

Currently Exploring:

02. Where I have worked

BUKC ACCP
Arts Team Head
Private Tutor
Grades 5-11

Arts Team Head

@ BUKC ACCP Club

2026 - Present

Leading the creative initiatives and visual identity for the Bahria University Karachi Campus (BUKC) ACCP Club, driving student engagement through impactful design and event coordination.

Creative Direction & Event Management:
Oversaw all creative direction and ensured the successful, timely delivery of university events and club activities.
Team Collaboration:
Coordinated with club members to conceptualize and execute visual assets that align with the organization's objectives and enhance campus presence.

03. Capstone Projects

Featured Project

BU-Chatbot

The BU Chatbot transforms a static PDF into an interactive AI assistant designed to help students navigate the Bahria University Student Rulebook. This project leverages MongoDB Atlas for vector search, Groq for high-speed LLM inference, and Clerk for secure user authentication.

FastAPI
Clerk
MongoDB
Groq
LangChain
Docker
AWS

Featured Project

Anime Character Detector

Developed a zero-shot anime character detection and tracking system capable of recognizing and re-identifying characters without retraining for new classes. The system combines a fine-tuned DEIMv2 detector with a LoRA-adapted DINOv3 vision transformer for feature extraction, using a vector database and similarity search to match previously seen characters across images and videos. Integrated Norfair tracking for consistent identity tracking in video streams and deployed the pipeline with ONNX for efficient inference.

DEIMv2
DINOv3
Norfair
LanceDB
OpenCV
ONNX
GoogleColab

Featured Project

NYC Fare Predictor

Built a hybrid NYC taxi fare prediction system using 40+ million trip records. Combined data-driven business rules for fixed charges with multiple machine learning models for uncertain fare components such as base fares, tips, and tolls. Designed an explainable prediction pipeline that delivers accurate fare estimates from a minimal set of trip inputs.

Numpy
DuckDB
SKLearn
XGBoost
Seaborn
FastAPI
HuggingFace

Featured Project

ViT Comparison

Benchmarked multiple adaptation strategies for DINOv3 to identify 26 One Piece characters from a custom dataset. Evaluated Frozen Features, Linear Probing, Fine-Tuning, LoRA, and LoRA + Supervised Contrastive Learning across different training data sizes. LoRA + SupCon consistently delivered the highest accuracy, reaching 97.7% top-1 accuracy while maintaining strong performance even in low-data scenarios.