{

"name": "Manu Venugopal",

"occupation": "AI Engineer",

"likes": ["Coding", "Football"],

}

Educational Background

University of Aberdeeen UK

Regularly presented new ideas in the machine learning group of the university.
Active participant of the DataLab events.

MSc in Artifical Intelligence 2021 - 2022

University of Calicut IND

BTech in Computer Science 2007 - 2011

Winner of Code Debugging, Gaming and Project Presentation.

NLP Data Scientist 2023 - Present

Advanced Deep Learning Models: Engineered sophisticated deep learning models using Hugging Face Transformers to efficiently categorize unannotated text, showcasing my ability to handle complex NLP challenges.

OpenAI Technologies: Utilized OpenAI technologies for a diverse range of NLP tasks, including text summarization, classification, search, and sentiment analysis, enhancing operational diversity and demonstrating adaptability to various project needs.

Innovative Model Enhancements: Achieved significant model enhancements by introducing innovative features, which boosted performance and secured two new clients, directly contributing to a 3% increase in company revenue.

Optimized LLMs: Applied cutting-edge techniques like Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) to optimize Large Language Models for critical NLP tasks, resulting in superior model efficiency.

Few-Shot Classification Model: Recognized internally for developing a state-of-the-art few-shot classification model that achieved remarkable sentence classification accuracy of over 98%, underscoring my technical prowess and innovative thinking.

Cloud and NLP Proficiency: Leveraged AWS and Azure cloud services, along with advanced NLP models such as Hugging Face’s Mistral AI and SetFit classifier, and OpenAI technologies. Demonstrated proficiency in Python and PyTorch for developing, optimizing, and deploying machine learning solutions.

Client Appreciation: Received client appreciation for developing an advanced meeting notes and summarization tool (using OpenAI and LangChain) reflecting my commitment to delivering impactful and user-centric solutions.

Senior Software Engineer 2016 - 2021

Senior Software Engineer 2015 - 2016

User Action Test Frameworks: Developed comprehensive test frameworks for the Configuration Desk tool components. This included areas such as configuration, IO function topology, model topology, hardware topology, and device topology, ensuring thorough and efficient testing processes.

Automated Test Cases: Implemented Python-based test cases to automate the verification of Configuration Desk components. Additionally, utilized C# with XML input files to create a test framework leveraging the N-Unit framework and Test Driven .Net add-in, significantly enhancing testing efficiency and accuracy.

Client Appreciation Awards: Honored with client appreciation awards twice for writing excellent test automation code and for creating an internal tool that generated quality test cases for various configuration inputs. These accolades reflect my dedication to delivering high-quality solutions and my ability to meet and exceed client expectations.

Cross-Functional Team Collaboration: Worked closely with diverse teams to develop innovative solutions aimed at improving business processes and boosting overall efficiency. This collaborative approach fostered a dynamic and productive work environment.

Sales Forecasting and Customer Segmentation: Revolutionized sales forecasting and customer segmentation by designing and implementing cutting-edge machine learning algorithms. These advancements significantly enhanced the accuracy of predictions and provided actionable insights, driving better business decisions.

NLP Model Development: Spearheaded the development of advanced Natural Language Processing (NLP) models for sentiment analysis and text classification. These models enhanced the understanding of customer feedback and market trends, allowing for more informed strategic planning.

FinFame Award: Honored with the prestigious FinFame award for successfully rectifying 35 pre-existing bugs and delivering an outstanding year-end project. This recognition underscores my commitment to quality and excellence in project delivery.

Hackathon Success: Achieved runner-up status in the Finatra Lake Mary USA hackathon, demonstrating my ability to excel in competitive, fast-paced environments and my aptitude for innovative problem-solving.

My Work Experience

My Works

Few shot prompt using OpenAI

Portfolio

Project Overview: Developed a few-shot classification model using OpenAI's advanced language models, enabling accurate classification with minimal labeled data.
Model Design: Leveraged OpenAI's powerful natural language processing capabilities to design a few-shot classification system, significantly reducing the need for extensive training datasets.
Performance: Achieved remarkable classification accuracy of over 98%, demonstrating the efficacy of few-shot learning in real-world applications.
Implementation: Utilized OpenAI's GPT-3 and later models, applying state-of-the-art techniques to fine-tune the model for specific classification tasks.
Impact: Enabled rapid deployment of classification solutions in scenarios with limited labeled data, enhancing operational efficiency and decision-making processes.
Recognition: Received internal recognition for innovation and effectiveness in applying few-shot learning to complex classification problems.

Github

Speech to Text using OpenAI Whisper

Project Overview: Developed a highly accurate speech-to-text system using OpenAI's Whisper model, transforming spoken language into written text with exceptional precision.
Model Implementation: Utilized the OpenAI Whisper model, known for its robust speech recognition capabilities, to convert audio inputs into text across various languages and accents.
Accuracy: Achieved state-of-the-art transcription accuracy, significantly reducing errors compared to traditional speech recognition systems.
Use Cases: Applied the Whisper model for diverse applications including transcribing meeting notes, generating subtitles for videos, and assisting in real-time communication tools.
Integration: Seamlessly integrated the speech-to-text functionality into existing workflows, providing users with an intuitive and efficient way to handle audio data.
Impact: Improved productivity and accessibility by enabling accurate and automated transcription, thus facilitating better documentation and information retrieval.
Recognition: Received positive feedback for the system's reliability and accuracy, leading to increased adoption and satisfaction among end-users.

Github

LangChain Bootcamp

Project Overview: Conducted an intensive LangChain Bootcamp covering core topics and advanced techniques to empower participants with comprehensive knowledge of LangChain.
Model: Explored various models supported by LangChain, focusing on selecting and fine-tuning models for specific use cases.
Data Connection: Demonstrated how to connect and integrate different data sources with LangChain, ensuring seamless data flow and accessibility.
Chains: Explained the concept of chains in LangChain, teaching how to build and manage complex workflows that automate sequential tasks.
Accuracy: Achieved state-of-the-art transcription accuracy, significantly reducing errors compared to traditional speech recognition systems.
Use Cases: Applied the Whisper model for diverse applications including transcribing meeting notes, generating subtitles for videos, and assisting in real-time communication tools.
Integration: Seamlessly integrated the speech-to-text functionality into existing workflows, providing users with an intuitive and efficient way to handle audio data.
Impact: Improved productivity and accessibility by enabling accurate and automated transcription, thus facilitating better documentation and information retrieval.
Recognition: Received positive feedback for the system's reliability and accuracy, leading to increased adoption and satisfaction among end-users.
Memory: Covered the memory capabilities of LangChain, including how to store and retrieve information across sessions to maintain context and enhance interactions.
Agents: Introduced the use of agents within LangChain, showing how to create intelligent agents that can autonomously perform tasks and make decisions based on provided data and context.
Hands-On Projects: Facilitated hands-on projects and practical exercises for participants to apply the concepts learned, ensuring a practical understanding of LangChain's capabilities.
Outcomes: Equipped participants with the skills to leverage LangChain for building sophisticated AI-driven solutions, enhancing their ability to tackle real-world challenges effectively.

Github

Certificate

Finetune LLM using LoRA and QLora

Project Overview: Developed a robust and efficient fine-tuning pipeline for a Large Language Model (LLM) using Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), optimizing model performance and reducing computational requirements.
Model: Conducted experiments using the Falcon-7B model as the base LLM.
Accuracy: Achieved state-of-the-art transcription accuracy, almost equals to the accuracy when training the entire nuerons in LLM.
Use Cases: Applied the fine-tuned model to various tasks, including generating responses to custom datasets and performing inference on specific queries. Demonstrated the model's versatility in handling diverse language processing tasks.
Integration: Seamlessly incorporated the fine-tuned LLM into existing AI workflows, ensuring smooth deployment and utilization in practical applications.
Impact: Enhanced model efficiency, making it feasible to deploy LLMs in resource-constrained environments. Reduced model size when compared to model after training entire neuron, thus making it executable with system with lower GPU configurations.
Recognition: Received positive feedback for the innovative approach and practical applicability of the project, leading to increased interest and adoption.

Github

Extracting complex information from PDF

Project Overview: Developed a system to extract text from PDF documents with complex structures like tables and format the output in JSON.
Model: Used OpenAI models and LangChain to accurately extract text from tables within PDF files.
Libraries Used: Used PyPDF2 from PyPDFLoader for basic text extraction from straightforward PDF layouts and Leveraged PyMuPDF (fitz) from PyMuPDFLoader for advanced extraction from PDFs with complex layouts and images.
Extraction Process: Combined PyPDFLoader and PyMuPDFLoader to process PDFs, and used OpenAI models to extract and structure text in JSON format.
Impact: Enhanced text extraction capabilities for complex PDF documents, enabling structured JSON outputs for improved data integration and workflow efficiency.
Recognition: Received positive feedback for significantly improving the accuracy and efficiency of text extraction from complex PDFs, leading to increased adoption and satisfaction among users who handle large volumes of unstructured documents.

Github

Conversationsal chatbot using RAG Approach

Project Overview: Developed a RAG (Retrieval-Augmented Generation) conversational chatbot that processes and understands content from supplied PDF files, enabling users to interact and query based on the document content.
Model Implementation: Utilized OpenAI's models to process and extract text from PDF documents, convert it into vector embeddings, and set up a conversational retrieval system.
Accuracy: Achieved precise text extraction and reliable vector representation of document content, facilitating accurate and context-aware responses to user queries.
Use Cases: Applied the chatbot for various applications such as customer support, information retrieval from technical manuals, and academic research assistance.
Integration: Seamlessly integrated the chatbot into a user-friendly Streamlit UI, allowing users to upload PDF files and interact with the extracted content through a conversational interface.
Recognition: Received positive feedback for the innovative approach and practical utility of the chatbot, leading to increased user engagement and satisfaction.

Github

Chatbot with Text to Speech using RAG

Project Overview: Project Overview: Developed a user-friendly Streamlit-based RAG conversational chatbot that integrates text-to-speech capabilities, utilizing OpenAI ADA embeddings and OpenAI's chat models.
Model Implementation: Employed OpenAI models for embedding and language understanding, integrated with a vector database for efficient information retrieval.
Accuracy: Achieved high precision in retrieving and generating contextually relevant responses from the PDF content.
Use Cases: Used for interactive document querying, virtual assistants, and educational tools.
Integration: Integrated into a Streamlit UI, allowing users to upload PDFs, query them, and receive spoken responses.
Impact: Enhanced user interaction with documents by providing an intuitive, voice-enabled querying experience.
Recognition: Received positive feedback for improving document accessibility and user engagement through conversational AI and text-to-speech integration.

Github

Exploring powerful Autogen (agent from system Microsoft)

Project Overview: Developed an automated system using Autogen to plot stock price changes, enhancing data visualization capabilities.
Model Implementation: Utilized Autogen's configuration and agent system to automate tasks and generate plots.
Accuracy: Accuracy: Ensured accurate data visualization by retrieving and plotting stock prices for META and TESLA.
Use Cases: Applied to financial data analysis, enabling automated generation of stock price charts for better decision-making.
Integration: Integrated into an automated workflow, allowing seamless interaction between user proxies and Autogen assistants for task execution.
Impact: Improved efficiency in data visualization and analysis by automating the process of plotting stock prices.
Recognition: Received positive feedback for streamlining financial data visualization, enhancing user experience and productivity.

Github

Multi-Agent Business Consultant using CrewAI and Streamlit

Project Overview: Developed a multi-agent business consultant application leveraging CrewAI and Streamlit to provide comprehensive business advice through interactive consultations.
Model Implementation: Utilized CrewAI to coordinate multiple agents, each specialized in different business domains, to offer holistic advice to users.
Accuracy: Ensured precise and relevant business recommendations by integrating domain-specific expertise within the multi-agent framework.
Use Cases: Applied to diverse business scenarios including financial planning, marketing strategy, and operational optimization.
Integration: Incorporated into a Streamlit UI, providing users with an intuitive platform to interact with the business consultant agents.
Impact: Enhanced decision-making processes for businesses by offering multi-faceted and expert-driven advice through a user-friendly interface.
Recognition: Received positive feedback for improving the accessibility and quality of business consultancy services, leading to higher user satisfaction and engagement.

Github

Summarization and Key point extraction using GPT4o

Project Overview: Developed a system to summarize conversations and extract key points from dialogues, using a sample conversation between a financial agent and a customer seeking low-risk investment advice.
Model Implementation: Employed LangChain and OpenAI GPT-3.5 Turbo and GPT-4o models to extract summaries and key points from the transcript.
Accuracy: Validated the model's output against the transcript to ensure accuracy and prevent hallucinations, identifying specific parts of the transcript corresponding to each key point.
Use Cases: Applied for summarizing financial consultations, extracting actionable advice, and improving documentation processes.
Integration: Compared the results with legacy models like GPT-3.5 Turbo and recent models like GPT-4o for performance evaluation.
Impact: Improved the reliability of conversation summarization and key point extraction, enhancing the efficiency of financial advisory services.
Recognition: Received positive feedback for enhancing accuracy and usability in summarizing financial consultations and extracting key insights.

Github

Certificate

About Me

I'm a passionate and versatile software engineer with proficiency in a diverse range of technologies, including PyTorch, C#, Python, TypeScript, HTML, and ASP.NET. My journey in the tech world has been marked by innovation, collaboration, and a commitment to excellence

Portfolio

One of my significant achievements includes co-creating an NLP application, where I not only implemented cutting-edge features but also prioritized robust security protocols to ensure user data integrity. This experience reflects my dedication to delivering solutions that not only push technological boundaries but also prioritize user trust and data privacy.

Beyond my core projects, I actively contribute to open-source initiatives, firmly believing in the power of knowledge sharing and collaborative development. It's my way of giving back to the tech community and staying engaged with the latest industry trends.