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AI Engineer with 1+ years in LLM, Voice AI & RAG.
Entry-level AI Engineer with hands-on experience building LLM-powered and Voice AI applications, Agentic RAG pipelines, and OCR-based document processing systems. Holds the DP-600: Microsoft Fabric Analytics Engineer certification and has shipped production-ready Python backends integrating Speech-to-Text, Text-to-Speech, and AI chat modules. Proficient in Python, LangChain, FastAPI, Hugging Face Transformers, and cloud-scale data pipelines. Passionate about applying AI to high-stakes, real-world problems in healthcare and enterprise workflows. Currently completing B.Tech in Computer Engineering (graduating 2026).
Sanjivani College of Engineering
B.Tech · Computer Engineering
August 1, 2022 – June 30, 2026
Residential College, Shevagon
HSC
June 1, 2021 – May 31, 2022
AI Genius LLP
AI Engineer Intern
November 1, 2025 – December 31, 2025
India
Agentic RAG Pipeline for Clinical Document Generation
February 1, 2026 – April 30, 2026
Designed a multi-agent RAG architecture using LangChain to ingest hospital IPD records (scanned PDFs and handwritten notes), extract structured data via Tesseract OCR, and store vector embeddings in a PostgreSQL pgvector store. Built an Agentic orchestration layer with a Planner agent, Retriever agent, and Summariser agent that collaboratively generate structured clinical summaries - reducing manual documentation effort significantly. Deployed the pipeline as a FastAPI microservice with fallback mechanisms (confidence thresholding, model switching) to handle low-quality scans and ensure production reliability. Evaluated end-to-end pipeline accuracy using ROUGE and BERTScore, iterating on prompts and retrieval strategies to improve summary fidelity.
View ProjectOCR-Based Medical Form Digitisation
December 1, 2025 – January 31, 2026
Built an automated OCR pipeline to digitise handwritten hospital intake forms using TrOCR (transformer-based OCR) achieving superior accuracy over baseline Tesseract on cursive handwriting. Applied OpenCV pre-processing (deskew, binarisation, noise removal) to improve raw OCR quality on low-resolution scans typically found in Indian hospital environments. Extracted structured fields (patient name, ward, diagnosis codes) into a PostgreSQL schema, enabling downstream analytics and EHR integration. Deployed as a FastAPI endpoint accepting image uploads and returning structured JSON — production-ready with input validation and error handling.
Real-Time Voice-to-Structured-Note System (VoiceBridge v2)
November 1, 2025 – January 31, 2026
Extended the VoiceBridge internship prototype into a production-grade Voice AI system capable of transcribing live speech, parsing medical entities (symptoms, medications, vitals) from free-text transcription, and producing structured JSON notes. Integrated Sarvam AI STT for Indian-language support alongside English transcription, supporting multilingual clinical conversations common in Karnataka hospitals. Built a WebSocket-based streaming layer in FastAPI to deliver real-time transcription with sub-500 ms round-trip latency, suitable for tablet-first ward workflows. Implemented RLHF-style feedback loop: care-team corrections were logged and used to fine-tune post-processing prompts, improving entity extraction accuracy by ~22% over 4 weeks.
Delhi AQI Prediction — ML Pipeline with Inference Optimisation
September 1, 2025 – October 31, 2025
Built an end-to-end ML pipeline to predict daily AQI levels using historical pollution sensor data; improved prediction accuracy ~18% over baseline using ensemble methods (Random Forest + XGBoost). Applied feature importance analysis to identify key pollutants (PM2.5, NO2) — demonstrating ability to derive actionable insights from model outputs. Optimised inference latency by caching feature transformations and batching predictions — skills directly applicable to low-latency AI module deployment.
DP-600: Microsoft Fabric Analytics Engineer
Microsoft
June 1, 2026 – Present
Python Programming Certification
Infosys
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects are highly relevant to the target role of an AI Engineer, showcasing a strong passion for AI and its practical applications. The diversity of projects (AQI prediction, OCR, Agentic RAG, Voice AI) demonstrates a broad interest and capability across different AI sub-domains. The focus on healthcare applications aligns well with roles requiring impactful, real-world solutions. The candidate's proactive pursuit of certifications and involvement in complex projects during their B.Tech indicates a strong drive for continuous learning and contribution, which is a good cultural fit for innovative teams.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, problem-solving, adaptability, ownership, and time management skills through their project descriptions and internship experience. Their focus on real-world problems, particularly in healthcare, indicates a practical and impact-driven approach. The ability to work on diverse projects and integrate various technologies suggests good operational fit for dynamic AI engineering roles.