Hire Muneeb Rashid

Muneeb Rashid

Big data processing
Product Manager

Employee Since

May 7, 2015

I’m a seasoned AI engineer specializing in computer vision. My expertise lies in crafting high-performing models for object detection, tracking, and image analysis, bringing intelligent vision to various domains. From optimizing real-time performance in autonomous vehicles to building scalable solutions for medical imaging, I thrive on tackling complex challenges through the power of computer vision. As a firm believer in MLOps practices, I ensure seamless model integration and continuous improvement. Driven by a desire to push the boundaries of this technology, I’m eager to contribute my skills and collaborate on projects with real-world impact.

Skills
  • Angular
  • AWS SES
  • Azure API Management
  • Concurrent Programming
  • Django Rest Framework
  • HTML 5 / CSS3
  • JQuery
  • Lamda
  • LINQ
  • MERN Stack
  • Big data processing
  • C++
  • CI/CD Pipelines (Jenkins, Github Actions)
  • Cloud computing (AWS)
  • Computer vision
  • Data mining
  • Data preprocessing
  • Data visualization
  • Deep learning
  • Dimensionality reduction
  • Docker
  • Faiss
  • Feature engineering
  • Fine-tuning of LLM
  • Hyperparameter tuning
  • Langchain
  • Llama index
  • Machine learning
  • MLOPS
  • Model Monitoring & Logging (MLflow)
  • Model Optimization
  • Model selection and evaluation
  • Natural language processing (NLP)
  • NLTK
  • PEFT
  • Pinecone
  • Python Programming
  • Pytorch
  • RAG
  • Recommender System
  • Semantic Search
  • Spacy
  • SQL
  • Statistical analysis
  • Supervised learning
  • Tensorflow
  • Time series analysis
  • Transfer learning
  • Unsupervised learning
  • Vector Embeddings
  • Virtual Assistant
Expertise
  • Natural Language Processing (NLP)
  • Python
  • Tensorflow
  • Pytorch
  • Llama 2 Language Model
Project Highlights

Conversational Chatbot using LLM and Semantic Chain (Llama 2,LangChain):

  • Leveraging the power of the Llama 2 language model and the LangChain framework.
  • I built a conversational chatbot capable of engaging in natural language interactions.
  • Utilizing techniques like tokenization, intent recognition, and response generation.
  • The chatbot learns from previous interactions to provide relevant and informative responses.
  • The project demonstrates the potential of large language models for building engaging and intelligent chatbot applications.

Technologies:

  • Natural Language Processing (NLP)
  • Python,
  • Pytorch,
  • Tensorflow,
  • Llama 2 Language Model,
    LangChain Framework,
  • Pytorch

Intelligent Information Retrieval System:

  • Designed and implemented an Intelligent Information Retrieval System using advanced NLP techniques to enhance document search and retrieval efficiency.
  • The system incorporates semantic analysis, document summarization, and relevance scoring for precise and context-aware information retrieval.

Technologies:

  • NLP
  • Python
  • Pytorch
  • Tensorflow
  • Word Embeddings
  • Text Summarization
  • NLTK
  • BERT
  • Semantic Search

Real-time AI-powered Theft Detection System using Computer Vision:

  • Real-time video analysis with computer vision detects suspicious activities like unauthorized access and item removal.
  • MLOps pipeline ensures continuous model training and deployment for optimal accuracy and adaptability.
  • Customizable alerts and notifications empower security teams for swift intervention and loss prevention.

Technologies:

  • NLP
  • Python
  • Pytorch
  • Tensorflow
  • transformer
  • Opencv
  • CNN
  • LSTM
  • Github Actions
  • CI/CD Pipeline
  • MLops

Continuously Evolving Traffic Sign Recognition System with Active Learning:

  • Trains a computer vision model for recognizing traffic signs in real-time.
  • Utilizes active learning to identify uncertain predictions and query human annotators for data labeling, improving model accuracy over time.
  • MLOps pipeline automates data acquisition, active learning loop, and model deployment for dynamic adaptation to changing traffic environments.

Technologies:

  • Python
  • Pytorch
  • Tensorflow
  • OpenCV
  • MLflow
  • NVIDIA Triton

Machine Translation using Seq2Seq:

  • Developed a machine translation system using a sequence-to-sequence (Seq2Seq) model to translate Urdu to English and vice versa.
  • Implemented an encoder-decoder architecture with an attention mechanism, achieving accurate and fluent translations between the two languages.

Technologies:

  • Python
  • Pytorch
  • Tensorflow
  • NLTK
  • Transformers
  • Hugging face
  • LSTM
  • RNN
  • Pytorch

Sentiments Analysis Using BERT:

  • Implemented a sentiment analysis system using BERT (Bidirectional Encoder Representations from Transformers), a powerful pre-trained language model.
  • Developed a pipeline that analyzes text inputs and determines the sentiment polarity (positive, negative, or neutral), aiding in understanding and classifying sentiment across different domains.

Technologies:

  • Python
  • Pytorch
  • Tensorflow
  • Hugging Face
  • Tensorboard
  • NLTK
  • Spacy

Marathon Participant Retrieval:

  • Designed and implemented an image clustering algorithm for identifying and grouping participants in a marathon based on the numbers on their shirts.
  • Utilized clustering techniques to group similar images, enabling efficient retrieval and organization of participant data for event management purposes.

Technologies:

  • Python
  • Pytorch
  • Tensorflow
  • Pillow
  • Face Recognition
  • Agglomerative clustering
  • Easyocr
  • Gradio Tensorflow

Scalable Real-time Object Tracking with Efficient Inference

  • Tracks objects in live video feeds using containerized models for efficient, distributed inference.
  • CI/CD pipeline and MLflow integration ensure rapid deployment and performance monitoring.
  • Enables high-demand applications like traffic analysis, crowd monitoring, and security surveillance.

Technologies:

  • Python
  • Pytorch
  • Tensorflow
  • Docker
  • CI/CD Pipeline (Github Action)
  • MLflow
  • Model Pruning/Quantization