● Spearheading the design and development of innovative Proof of Concepts (POCs) for new features and complex architectures within the LLM pipeline. Leveraging deep understanding of machine learning principles and architectural best practices to create scalable and efficient solutions.● Engaging in cutting-edge research involving RAG (Retrieve, Analyze, Generate) pipeline and support docs architecture. Contributing insights and expertise to drive advancements in research methodologies and architectural design within the organization.● Orchestrating the development and deployment of cutting-edge NLP and LLM models for Generative AI applications, text classification, information extraction, NER extraction, text clustering, and text summarization. Employing sophisticated techniques and Python libraries such as spaCy, NLTK, and transformers to achieve groundbreaking results.● Automated the data science platform by building and managing data pipelines and extensions that bridges NoSQL-databases, APIs and Machine Learning Engine.● Developed a dynamic LLM pipeline that efficiently incorporates new features and requirements by optimizing prompt engineering. Extensively utilized a vector database for grouping and clustering, with integrated semantic search functionality to enhance retrieval and accuracy.

● Working on cutting-edge language models LLMs such as GPT-3, GPT-4, Llama 2, and Anthropic models (Claude Instant-1, Claude 3 – Haiku) and many more to extract business intelligence dimensions from complex data using pinecone vector database, facilitating effective data analysis and enhancing decision-making processes. Employing Python for model fine-tuning and adaptation to specific business requirements.● Leveraging advanced natural language processing techniques in Python to extract and categorize customer CEH (Complaints, Enquiries, and Helpdesk) data, providing valuable insights into customer behavior and preferences. Implementing custom Python scripts for data preprocessing and analysis, utilizing core Python knowledge for efficient code development.● Working on call analytics for call center data, utilizing Python-based speech recognition technology and various APIs for transcription and speaker classification to extract insights containing NLU. Developing a custom Python model for speech analytics to improve data extraction accuracy and quality, while leveraging core Python knowledge for efficient coding practices.● Utilizing AWS services like Code Commit, AWS Comprehend, AWS Lex, and AWS Sage Maker for development and seamless deployment, incorporating Python scripts and core Python knowledge for integration with cloud services.

proficient in ML algorithms like Regression, Classification, Clustering, and Dimensionality Reduction, as well as Deep Learning architectures including CNNs, RNNs, GANs, and Federated Learning. I use industry-leading frameworks like TensorFlow, PyTorch, Keras, Tf-agents, and Scikit-Learn.

Developed LSTM models for Rain Forecasting on GSMAP-18 Data and CNN models for ImageSegmentation on day-to-day and hourly images of Rain. Worked on highly non-linear chaotic systems in Deep-Data Assimilation using high-end GPUs to traindeep learning models. Conducted research on "Development of Deep Data Assimilation for Geophysical Model" based on the Kalman filter and particle filter method for data assimilation on atmospheric convection models like Lorenz-63 and Lorenz-96 in the Atmospheric Science Division under Scientist Dr. Prashant Kumar.

I work with students to help them in their final year project.along with that I train them how carried out full stack approach towards project work with web development, machine learning project and IOT based project.