Rajesh User

Developed and deployed a scalable LLM-based chatbot using Azure AI Foundary and AWS Bedrock, reducing response latency by 25% and improving user query resolution by 30% through fine-tuning and model optimization. Built and deployed a custom LLM solution using Hugging Face's Llama-2-7B model, fine-tuned on 50K domain-specific examples, and integrated with Langchain for RAG capabilities, achieving a

Developed Generative AI-powered chatbots using AWS Bedrock and fine-tuned LLMs (e.g., GPT-4, Falcon, T5) for real-time patient symptom analysis and personalized treatment recommendations. Implemented Retrieval-Augmented Generation (RAG) with FAISS and AWS OpenSearch to enhance chatbot accuracy by integrating real-time EHR and clinical trial data. Built secure, scalable, and compliant chatbot solut

Designed and executed scalable machine learning model training workflows using Amazon SageMaker, optimizing training performance through distributed processing. Managed a centralized Feature Store in Amazon SageMaker, enabling real-time feature retrieval and reuse across ML models, ensuring 99.9% uptime for consistent feature availability. Implemented Amazon SageMaker Model Monitor to detect data

Developed predictive models using Python (pandas, NumPy, SciPy, scikit-learn) for credit risk and fraud detection. Designed and implemented end-to-end machine learning pipelines using PySpark and Databricks Notebooks for sales forecasting. Automated CI/CD pipelines with Jenkins, AWS CodePipeline, and Docker, ensuring seamless model deployment.

Conducted customer segmentation using K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA). Developed Python-based data pipelines using pandas, NumPy, and PySpark, processing large financial datasets for risk modeling. Deployed REST APIs with FastAPI and Flask on Azure App Services for real-time analytics.

Automated routine telecom tasks like log file cleanup and data extraction from network systems using Python scripts. Supported the team in processing telecom data (CDRs, SMS logs) using Python to identify call patterns and prepare reports for analysis. Assisted in testing and debugging Python code written by senior developers, which helped me learn clean coding and problem-solving skills.

Completed a Masters degree in Computers, focusing on advanced topics and research within the field. Gained in-depth knowledge and practical skills relevant to the latest technological advancements.