m Tahir Munir

Gen AI | Custom LLM | Full Stack Developer | Data Science Enthusiast

With a passion for AI and a keen interest in Data Science  Now, my focus has expanded to encompass AI applications and the fascinating world of Data Science. I’m on a continuous quest to deepen my understanding of LLM models, Custom LLM solution, Langchain, Vector database, machine learning and data analysis, leveraging my development expertise to create innovative solutions at the intersection of AI and web development.

My journey began as a Full Stack Developer, where I honed my skills in crafting robust applications using technologies like ReactJS, Typescript, NodeJS, and ExpressJS. I’ve delved into database management with MongoDB and Firebase, ensuring seamless data handling for applications.

 

Professional Career Summary

Now, I’m diving into AI and Data Science, blending my skills for exciting new projects.

Datascience Skills

Online certification in Complete Generative AI Course With Langchain and Huggingface

Generative AI & Langchain Integration:

  • Langchain Setup: Creating environments and getting started with Langchain for AI development.
  • Langsmith: Integration of Langsmith for tracking and managing generative AI models across applications.
  • Langraph: Implementation of Langraph for graph-based LLM integration and understanding model flows.
  • LLM Integration: Expertise in integrating various LLM models from OpenAI, Huggingface, and Azure OpenAI for real-time applications.
  • OpenAI and Huggingface Integration: Using pre-trained models and embeddings with Langchain.
  • Retrieval-Augmented Generation (RAG): Building RAG pipelines for efficient question-answering systems.
  • Building Generative AI Applications: Developing applications like chatbots, text summarizers, and Q&A systems.
  • Prompt Engineering: Creating and managing prompts and conversation history in generative AI applications.
  • Building and Deploying Chatbots using Langchain and Streamlit.

Integration of Large Language Models (LLMs) & AI Tools:

  • OpenAI: Use of GPT, GPT-3, GPT-4 LLM etc
  • Hugging Face: Expertise with the Transformers library and various open-source models
  • Meta: Skilled in LLaMA model integration
  • Groq: Skilled in Groq’s AI systems integration
  • Crew AI: Experience with Crew AI platform/tools
  • Google: Familiar with Gemini
  • Anthropic: Familiar with Claude LLM
  • Microsoft: Azure OpenAI Service and Copilot integration
  • Nvidia: Familiar with NeMo Megatron

Natural Language Processing (NLP):

  • Text Processing Techniques: Expertise in text classification, tokenization, stemming, lemmatization, Bag of Words (BoW), N-grams, One-Hot Encoding, and TF-IDF
  • Word Embeddings: Proficient in Word2Vec (CBOW, Skip-gram) for semantic understanding
  • NLP Applications: Skilled in Sentiment Analysis, Named Entity Recognition (NER), and Part-of-speech (POS) tagging
  • Libraries & Tools: Extensive experience with popular NLP libraries such as NLTK and SpaCy

Machine Learning & Deep Learning Skills:

  • Frameworks: Proficient in TensorFlow, PyTorch, and Keras for developing and training deep learning models
  • Neural Network Techniques: Skilled in forward and backward propagation for training Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN)
  • Model Architectures: Expertise in a range of deep learning architectures including ANN, RNN, Long Short-Term Memory (LSTM), Bidirectional RNN, Gated Recurrent Units (GRU), and Attention Mechanisms

Data Handling & Feature Engineering:

  • Data Preprocessing: Extensive experience in data cleaning, preprocessing, and feature engineering for machine learning models
  • Feature Transformation: Skilled in feature transformation techniques using Scikit-learn (Sklearn)
  • Dimensionality Reduction: Proficient in applying Principal Component Analysis (PCA) for reducing dimensionality and optimizing model performance

Vector Stores & Embeddings:

  • Vector Databases: FAISS, ChromaDB, Pinecone, AstraDB, GraphDB, Neo4j
  • Embeddings: OpenAI, Ollama, Hugging Face etc
  • Techniques: Expertise in cosine similarity for recommendation systems and search optimization

Graph Database Technologies:

  • Graph Databases: Neo4j, Neo4j AuraDB
  • Query Languages: Worked with Cypher Query Language, GraphQL

Advanced Concepts:

  • Transformer Architectures: In-depth knowledge of Transformers, including Multi-Head Attention, Self-Attention, and Positional Encoding
  • Model Architectures: Expertise in Encoder-Decoder models and the Attention Mechanism
  • Optimization Techniques: Proficient in Layer Normalization and model quantization methods such as LoRA and QLoRA for efficient fine-tuning and deployment

Deployment & Integration:

  • Web Application Integration: Experience with building and deploying interactive applications using Streamlit
  • Cloud Deployment: Proficient in deploying AI models on AWS Cloud, Hugging Face Spaces, and Streamlit Cloud
  • API Development: Skilled in API development and deployment using Langserve

Worked on these Projects:

  • End-to-End Chatbot Development: Built a conversational chatbot using Langchain, Huggingface models, and RAG pipelines for accurate question-answering.
  • AI-powered Text Summarization Tool: Developed a web app using Streamlit and Langchain to summarize YouTube video transcripts and web pages.
  • NLP-Based Sentiment Analysis: Implemented deep learning models for text classification and sentiment analysis using RNN, LSTM, and Transformer architectures.
  • Hybrid Search Engine: Created an AI-powered hybrid search engine using Pinecone DB and Langchain for document retrieval with integrated Q&A capabilities.
  • Neo4j and Graph Databases: Creating and querying property graph data models using Cypher.
  • AWS Integration: Basic understanding of services like AWS Bedrock, Lambda, and SageMaker for generative AI projects.

Web Application Development

Aws server management