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AI/ML Engineer | Microsoft Certified Data Scientist Associate | LLMs • RAG • Agents • Fine-tuning | End-to-End ML Pipelines | Ex-EY GDS | Building Real-World AI
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EY
Data Scientist
June 28, 2026 – Present
credit-risk-mlops-pipeline
June 21, 2026 – Present
An enterprise-grade, configuration-driven MLOps pipeline for credit risk underwriting. Built with XGBoost, strict data validation, mlFlow, and CI/CD automation. Dockerized inference deployed via render
View ProjectAwadh_AI-Agentic_Lucknow_Travel_Planner
October 30, 2025 – Present
An enterprise-grade, full-stack AI travel planner which provides data-driven itineraries for Lucknow, India and showcases production-ready architecture, combining a FastAPI backend with a Streamlit frontend. It leverages an advanced agentic RAG system, context-aware responses by integrating a local knowledge base with live, external APIs.
View Projectmedical-rag-chatbot-llm
September 16, 2025 – Present
A conversational AI medical chatbot built with a RAG pipeline using LangChain, Groq, and Streamlit. This project ingests medical documents to provide fast, context-aware answers to user queries.
View Projectproduction-ready-ml-pipeline
August 5, 2025 – Present
This project demonstrates a complete end-to-end Machine Learning workflow, designed to replicate industry-grade ML system design and architecture. The core objective is to predict the quality of wine based on various chemical and physical parameters such as acidity, sugar content, pH, and alcohol levels
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong alignment with the Data Scientist role, particularly in areas of MLOps, AI agents, and RAG systems. The diversity of projects (travel planner, credit risk, medical chatbot, wine quality prediction) indicates a broad interest and ability to apply data science across different domains. The focus on 'enterprise-grade' and 'production-ready' systems suggests a proactive approach to building robust solutions, which aligns well with a results-oriented culture. However, the lack of team-based projects or contributions to open-source initiatives limits the assessment of collaborative cultural fit.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a focus on building production-ready systems and understanding enterprise-grade architectures, suggesting a strong operational fit. However, without psychometric test results or interview data, soft skills like teamwork, problem-solving under pressure, and communication cannot be fully assessed.