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.