● Developed optimized activities for agents, worked on SLA processing, and extensively managed le listener processes to automate interactions with external systems.
● Designed and implemented validation and integration rules, focusing on RuleConnect and Rule Service.
● Played a key role in designing high-level Class structures, contributing to rule set management and group management processes.
● Successfully developed a Personal Loan application using Pega, enhancing the efficiency and accuracy of the loan processing workflow.
● Collaborated with cross-functional teams to deliver high-quality software solutions on time.
● Provided technical support and troubleshooting for Pega-based applications, ensuring smooth operations.
Project: FAQ-Based RAG Chatbot.
● Developed a RAG FAQ Chatbot using LangChain, FAISS and ChromaDB for handling FAQ-based queries. Integrated both vector databases to optimize data retrieval and improve chatbot response accuracy.
● Implemented PDF-based document processing, allowing users to upload files and store embeddings for retrieval.
● Utilized Hugging Face embeddings and a Flan-T5 language model for accurate response generation.
● Built a FastAPI backend with endpoints for PDF uploads and query-based retrieval.
● Applied text chunking techniques using RecursiveCharacterTextSplitter to enhance retrieval accuracy.
● Gained hands-on experience in Vector Databases, LLMs, FAISS, and API development.
Project: Intent-Based Chatbot.
● Developed a rule-based chatbot to handle predefined user intents such as greetings, product inquiries, and pricing details.
● Implemented intent-response mapping for structured and efficient query handling.
● Used Gradio to build an interactive user interface for real-time chatbot interactions.
● Applied Python and NLP techniques to improve chatbot response accuracy.
● Gained hands-on experience in chatbot development, user interaction design, and Gradio deployment.
Project: Image Generation.
● Built a generative model leveraging Stable Diffusion to create high-quality images from textual descriptions.
● Utilized PyTorch and Hugging Face Diffusers library to implement the architecture and manage training workflows.
● Gained expertise in Generative AI, Stable Diffusion Models, and advanced Python programming for model implementation.
Project: Object Detection.
● Designed and implemented an object detection system using the YOLO (You Only Look Once) algorithm to identify and classify objects in real-time.
● Used OpenCV and PyTorch to deploy the model for real-world applications, including live video feeds.
● Gained hands-on experience in Computer Vision, Deep Learning, and efficient object detection frameworks.
Project: Image Classification.
● Developed an image classification model using MobileNetV2 as a feature extractor.
● Applied data augmentation techniques (rotation, shifting, zooming, flipping) to enhance model generalization.
● Utilized TensorFlow & Keras to train and fine-tune the model for improved accuracy.
● Processed images using ImageDataGenerator for efficient loading and augmentation.
● Evaluated model performance using test datasets and optimized hyperparameters for better results.
● Gained hands-on experience in Transfer Learning, Deep Learning, and
● Image Processing.
Project: Movie Recommendation System.
● Developed a content-based recommendation system using a dataset of Telugu movies.
● Utilized TF-IDF (Term Frequency-Inverse Document Frequency) to extract key features from textual data and calculated cosine similarity to find relevant movies.
● Enhanced user experience by providing accurate and personalized movie recommendations based on user preferences.
● Gained hands-on experience in Python, Pandas, and Scikit-learn, with a focus on NLP techniques.
Project: NLP Tasks Using Pretrained.
● Developed a comprehensive NLP system to perform tasks such as Sentiment Analysis, Named Entity Recognition (NER), and Text Generation using pretrained Transformer models from the Hugging Face Transformers library.
● Integrated models like BERT for NER and sentiment analysis, and GPT-2 for text generation, leveraging transfer learning for rapid deployment.
