Sameer Rasekar

• Detail-oriented software engineer with over 4 years of experience in Python backend development and natural language processing (NLP).
• Skilled in implementing retrieval-augmented generation (RAG) techniques and ethical web crawling, enhancing application accuracy by approximately 60%.
• Proficient in API development and integration, utilizing machine learning for effective lead generation and intelligent chatbots creation improving customer satisfaction by 45 %.
• Experienced in prompt engineering and working with vector databases to optimize information retrieval and response accuracy.
• Familiar with technologies such as AWS and its various services.
• Committed to leveraging advanced tools to enhance user experience and streamline business processes.

https://www.linkedin.com/in/sameerrasekar/

Meter Reading and Bill Distribution (MRBD) - CESC   (Jan 2021 - Sep 2023 )

This project was developed to take a meter reading through mobile and upload data into a database with image validation from validator. It   consists of importing consumer data, scheduling, dispatching route to meter reader, validating the reading, uploading data to the server, distributing bills to consumers, system users and administration module.

AI-Based Chatbot with Knowledge Base Integration    (Sep 2023 - Present)

Developed an AI-powered chatbots that extracts information from PDFs, Word, Excel, and websites to build a knowledge base. The chatbot combines NLP and LLMs with strategies like chunking, Retrieval-Augmented Generation (RAG), and vector databases for semantic search. This enables the chatbots to generate accurate, context-aware responses from diverse data sources, making it ideal for knowledge management and customer support.

GenAI Automatic Email Bot     (Mar 2024 - Present)

The GenAI-Powered Automated Email Bot for ServiceNow is an AI-driven solution that reads incoming emails, classifies them using machine learning, and generates automated responses. It integrates with ServiceNow to fetch request statuses and respond to users, saving time and resources by streamlining email management.

Lead Generation Application (May 2023 - Present)

Our lead generation service meticulously tracks all queries, responses, and feedback received through our chatbot application. This data is then analyzed to uncover the intentions and motives behind the inquiries related to specific topics. By gaining these insights, we can effectively identify and target the right customers, ultimately saving time and resources. Moreover, this process enables us to deliver more curated responses to our customers, enhancing their overall experience. We achieve this through advanced techniques in Natural Language Processing, including summarization and sentiment analysis, along with prompt engineering using both single and multi-Large Language Models (LLMs). This improves the customer targetting by around 30 percent.

Excellence In Innovation

Sepia Innovations 2024-10-03

Description:

Honored with the Excellence in Innovation award for developing and implementing effective chunking and indexing strategies for Retrieval-Augmented Generation (RAG). This recognition reflects my efforts to enhance the accuracy of responses generated by Large Language Models (LLMs) through thoughtful prompt engineering, achieving an improvement of approximately 60%. I am grateful for the opportunity to contribute to advancements in natural language processing and am committed to continuing to explore innovative approaches that enhance the performance of AI-driven solutions.