• Highly motivated and results-oriented AI Engineer with around 6 years of experience building and deploying innovative AI solutions.
• Proven expertise in developing and implementing machine learning models, natural language processing (NLP) systems, and generative AI applications.
• Strong analytical and problem-solving skills with a passion for leveraging cutting-edge technologies to drive business value.

• Expertise in designing, developing, and deploying end-to-end AI solutions using Python and relevant libraries such as Rasa, Langchain, LangGraph, and NLTK.
• Proven ability to build and integrate conversational AI systems using tools like Rasa, AWS Lex, AWS Bedrock, and Amazon Connect, enhancing customer experiences and automating business processes.
• Experience in leveraging Gen AI techniques, including Retrieval Augmented Generation (RAG), to develop sophisticated applications like Question Answering bots and intelligent agents.
• Strong understanding of machine learning algorithms and deep learning architectures, with experience in model training, evaluation, and deployment.
• Proficient in working with cloud platforms like AWS, utilizing services such as AWS.
• Bedrock for deploying and managing AI models.
• Experience with vector databases (Pinecone, ChromaDB) for AI applications.
• Excellent communication and collaboration skills, with a proven track record of working effectively in cross-functional teams.

AI/ML Tools: AWS Bedrock, AWS Lex, Amazon Connect, Langchain, LangGraph, NLTK, H2O GPT, Rasa
Large Language Models (LLMs): Claude, Llama 2/3/3.1
Embedding Models: Cohere, AWS Titan
Cloud Technologies: AWS (EC2, S3, Lambda, Secrets Manager, RDS, IAM, EC2 with GPU), Google Colab 1
Other: Linux, Docker, Container Registry

Databases: SQL, NoSQL, VectorDB (Pinecone, ChromaDB)
Version Control: Git

Video Transcription (Q&A Bot):

Description: Developed a Question Answering system using Retrieval Augmented Generation (RAG), combining LLM foundation models with vector databases to enhance QA Bot accuracy and efficiency. The setup was entirely serverless, featuring a data cleaning and ingestion pipeline that splits text chunks and generates metadata.

Domain: Natural Language Processing, Question Answering Bot

Technologies Used: AWS Bedrock, AWS Lambda, S3, Secrets Manager, Pinecone, Python, RAG, Titan embeddings

LangChain Agentic Tool Calling Bot:

Description: Built an intelligent agent using Langchain to automate tasks by seamlessly integrating with external tools and APIs, streamlining workflows and boosting overall productivity.

Domain: Conversational AI, Task Automation

Technologies Used: AWS Bedrock, OpenAI, Python, Langchain, Cohere Embeddings, Knowledge base, Pinecone/ChromaDB

LangGraph Multi-Agent Customer Support System:

Description: Designed and implemented a multi-agent customer support system using LangGraph to handle customer queries efficiently and provide personalized responses.

Domain: Conversational AI, Customer Support

Technologies Used: AWS Bedrock, Python, LangGraph, Cohere Embeddings, Knowledge base, Pinecone/ChromaDB

RASA Chatbot:

Description: Developed a chatbot using the Rasa framework to automate customer interactions, provide instant support, and enhance customer satisfaction.

Domain: Conversational AI, Customer Service

Technologies Used: AWS Bedrock, Python, LangGraph, Cohere Embeddings, Knowledge base, Pinecone/ChromaDB

Omihub (Multi-Input Multi-Domain AI Agent):

Description: Built a versatile AI agent capable of processing input from multiple sources (text, voice, etc.) across different domains, showcasing proficiency in complex AI system development.

Domain: Artificial Intelligence, Natural Language Understanding

Technologies Used: Python, FastAPI, ReactJS, Django, NLTK, Cohere Embeddings, Knowledge base, Pinecone/ ChromaDB/ RDS/ DynamoDB/ MongoDB/ Sqlite, PostgreSQL

Freight Planner Bot (SQL Query + RAG + API):

Description: Developed a freight planning bot that leverages SQL queries, RAG, and API integrations to provide optimized freight itineraries and recommendations for air and sea transport.

Domain: Logistics, Freight Planning

Technologies Used: Python, SQL, RAG, React, FastAPI, LangGraph, Cohere Embeddings, Knowledge base, Pinecone/ChromaDB/RDS

NLTK-Based Question Answering System:

Description: Created a bot using NLTK to process user input prompts, search for matching questions in a dataset, and deliver accurate and relevant answers.

Domain: Natural Language Processing, Question Answering

Technologies Used: Python, NLTK

H2OGPT Exploration and Fine-tuning:

Description: Conducted exploration and fine-tuning of H2O GPT for various text generation tasks, gaining hands-on experience with state-of-the-art language models.

Domain: Generative AI, Language Modeling

Technologies Used: Python, H2O GPT, Llama 2/3/3.1