Generative AI Engineer with 2+ years in LLM-powered applications & AI solutions.
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Motivated AI/ML Engineer with hands-on experience building LLM-powered applications, RAG pipelines, and production-ready AI solutions. Proficient in LangChain, FAISS vector databases, Hugging Face Transformers, and FastAPI/Flask deployment. Passionate about developing intelligent chatbots, AI agents, and end-to-end GenAI workflows. Strong Python and API integration skills with a problem-solving mindset and collaborative approach.
Rajasthan Technical University
M.Tech · Computer Science & Engineering
August 1, 2024 – Present
Rajasthan Technical University
B.Tech · Computer Science & Engineering
August 1, 2017 – June 30, 2021
Energy Demand Forecasting – Automated AI Pipeline
November 1, 2024 – January 31, 2025
Developed a hybrid LSTM + XGBoost forecasting model on 5 years of hourly energy data, achieving 3.2% MAPE – outperforming ARIMA baseline by 41%. Automated weekly model retraining using Apache Airflow with data drift detection, reducing manual intervention by 80%.
Customer Churn Prediction – AI REST API
June 1, 2024 – August 31, 2024
Built an end-to-end ML pipeline using XGBoost and SMOTE on a 100K+ customer telecom dataset, achieving 91% ROC-AUC and 88% recall on the minority class. Developed a SHAP-based explainability dashboard identifying top 10 churn drivers; exposed predictions via a REST API built with Flask and containerized with Docker (<200ms response time). Estimated 18% churn reduction through model-driven interventions.
Blockchain Supply Chain Authentication System
January 1, 2024 – May 31, 2024
Architected a three-tier blockchain authentication system (React, Node.js/Express, MongoDB) with SHA-256 cryptographic hashing – achieving <50ms block creation time and 100% tamper detection accuracy. Engineered a QR-based product verification system with role-based access control for 5+ user types; deployed across 82 weavers in 3 cooperatives, delivering 23% price premium and 37% faster sales.
RAG-Powered Document QA System
January 1, 2024 – Present
Designed and built an end-to-end Retrieval-Augmented Generation (RAG) pipeline using LangChain, FAISS vector store, and Hugging Face sentence-transformers for semantic document retrieval. Integrated open-source LLMs via Hugging Face and implemented prompt engineering techniques to improve answer relevance and reduce hallucination. Deployed the pipeline as a FastAPI REST endpoint with Docker containerization for scalable serving.
Apache Spark 3
Databricks Certified Associate, Udemy
October 1, 2025 – Present
Data Visualization in Excel
Macquarie University, Coursera
November 1, 2024 – Present
IHUB AIML Certification
SimpliLearn
September 1, 2024 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong alignment with modern AI/ML development practices, including MLOps, explainable AI, and scalable deployment. The mix of professional and personal projects, especially the RAG-Powered Document QA System, shows initiative and a genuine interest in the Generative AI domain. The breadth of technologies used across projects (Python, JavaScript, various ML frameworks, Docker, Airflow) indicates a versatile engineer who can adapt to different tech stacks and contribute to diverse teams. The certifications further support a commitment to continuous learning and staying current with industry trends.
Soft Skills & Operational Fit
The candidate's professional summary highlights a problem-solving mindset and collaborative approach, which are crucial for operational fit in a senior engineering role. The project descriptions indicate an ability to work on complex, multi-faceted problems and deliver quantifiable results. The diversity of projects suggests adaptability and a willingness to tackle different technical challenges.