Shiva User

Led design thinking sessions for Generative AI use cases and developed multi-agent systems using AutoGPT, CrewAI, LangGraph, and Autogen. Engineered RAG applications, optimized prompt templates, and built real-time voice assistants and AI vision models, significantly improving efficiency and accuracy.

Developed multiple chatbots using Azure OpenAI, Langchain, Semantic Kernel, and AWS Bedrock, enhancing summarization and Q&A capabilities. Engineered hybrid search systems and deployed multi-agent systems with AutoGen, significantly improving data retrieval and operational efficiency.

Leveraged LLMs to build chatbots for sentiment analysis, topic modeling, and NER, increasing efficiency by 25%. Developed a binary classification model using XGBoost and Random Forest for fraud detection and built time series models for anomaly detection in financial logs.

Trained a Reinforcement Learning agent using PyTorch to optimize ATM revenue and developed a KerasNLP-based summarizer, reducing documentation review times. Built a TensorFlow-based sentiment analysis model for customer feedback and delivered a chatbot using Azure ML, boosting response efficiency.

Forecasted energy consumption using PyTorch LSTM models, reducing error rates by 22%. Applied RNN models in TensorFlow to predict manufacturing demand, decreasing inventory forecasting errors by 15%.

Pursued a Master of Science in Applied Computer Science. Focused on advanced topics within the field, enhancing skills in various computing disciplines.

Completed a Bachelor of Technology in Computer Science and Engineering. Gained foundational knowledge and practical skills in computer science principles and engineering applications.