Generative AI Engineer with less than a year in QLoRA & RAG pipelines
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Motivated AI/ML Engineer with practical experience in fine-tuning large language models (QLoRA, TinyLlama), building RAG pipelines, and deploying NLP-based systems. Adept at bridging research and engineering to deliver scalable, intelligent solutions. Eager to grow in a fast-paced environment focused on Generative AI and intelligent automation.
IICT, MGM University
Bachelor of Technology · Data Science
June 1, 2023 – June 1, 2026
CIPET
Diploma · Plastic and Mould Engineering
June 1, 2020 – June 1, 2023
MedInsight - Medical Report Explainer
June 24, 2026 – Present
Fine-tuned TinyLlama-1.1B on PubMedQA medical Q&A dataset using QLoRA 4-bit quantization on Google Colab T4 GPU. Built and trained a 3-layer PyTorch neural network classifier from scratch to detect abnormal blood test values, achieving 90.8% accuracy across 14 parameters. Implemented end-to-end RAG pipeline using FAISS vector store and sentence-transformers embeddings for context-aware, semantically grounded question answering. Designed an agentic reasoning loop that intelligently routes patient queries between uploaded report context and fine-tuned LLM general medical knowledge.
HireScope - Intelligent Resume Screening Analysis Tool
June 24, 2026 – Present
Built an AI-powered resume analysis platform delivering ATS scoring, keyword gap analysis, resume optimization, and personalized cover letter generation using large language models. Implemented a RAG pipeline with LangChain and HuggingFace embeddings for semantic resume-JD matching, with ranked candidate retrieval and confidence scoring via ChromaDB. Developed a FastAPI backend with REST endpoints for resume ingestion and LLM-based scoring using Groq LLaMA 3.1, paired with a multi-screen Streamlit frontend for real-time interaction. Containerized the full stack using Docker Compose and automated candidate shortlisting via N8N webhook pipelines with SMTP email notifications.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and practical application in Generative AI, aligning well with the target role. The diversity of projects (medical explainer, resume screening) shows adaptability and a broad application of AI skills. However, the lack of professional experience and formal team project descriptions makes a full cultural fit assessment challenging.
Soft Skills & Operational Fit
The candidate lists soft skills such as data-driven decision making, technical communication, analytical problem decomposition, team coordination, self-directed learning, and attention to detail. These indicate a potential for good operational fit and collaboration, though these are self-reported and not validated by assessments.