AI Engineer with less than a year in AI/ML Engineering & Multimodal Systems
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AI/ML Engineer specializing in agentic systems and LLM applications, with production-level experience building multimodal pipelines and multi-agent frameworks. Proficient in RAG, prompt engineering, and end-to-end ML pipelines using PyTorch, LangChain, and LangGraph, integrated with modern web and cloud technologies.
Woxsen University
B.Tech · Computer Science (AI & ML)
August 1, 2022 – June 30, 2026
SANSI RF and Communication Systems Pvt. Ltd.
Software Engineer Intern
March 1, 2026 – June 1, 2026
India
Infotact Solutions
Data Science & ML Intern
June 1, 2025 – September 1, 2025
India
MediScan - Agentic AI Copilot for Medical Image Diagnosis
February 1, 2026 – April 1, 2026
Developed a multimodal vision-language model combining DenseNet121 and Bio-ClinicalBERT via contrastive learning, achieving 87% Top-3 retrieval accuracy. Engineered a low-latency RAG pipeline using FAISS and Grad-CAM for interpretable, visually grounded clinical predictions. Designed a multi-agent system with Vision and Reasoning agents for automated pathology detection and structured report generation. Launched via Dockerized FastAPI, optimizing response latency and ensuring high availability across clinical workflows.
Autonomous Real Estate WhatsApp AI Agent & Advanced RAG Pipeline
January 1, 2026 – February 1, 2026
Architected an end-to-end conversational AI workflow in n8n that collects user preferences over WhatsApp and performs intelligent property matching based on budget, location, and requirements. Integrated Pinecone vector database with LangChain to enable semantic search over real estate listings, delivering accurate property recommendations during live chat sessions. Programmed the agent to query legal property data — covering deeds, occupancy certificates, and tax statuses — and explain them conversationally to users on request. Synchronized Airtable CRM and Google Calendar API to store structured user profiles and automate site visit scheduling directly through WhatsApp conversation flow.
Vision-Language Surveillance Search Engine (VLSSE)
November 1, 2025 – December 1, 2025
Created an AI-powered surveillance search engine enabling natural language queries over CCTV footage, reducing manual review time by over 60%. Implemented real-time person detection and multi-object tracking using YOLOv8 and ByteTrack with high identity continuity. Established a cross-modal retrieval pipeline using CLIP embeddings and FAISS for fast, accurate event lookup from unstructured video. Launched an interactive Streamlit dashboard for video ingestion, query-based search, and scene-level investigation by non-technical analysts.
IBM RAG and Agentic AI
Coursera / IBM
June 1, 2026 – Present
Building AI Agents and Agentic Workflows
Coursera / IBM
June 1, 2026 – Present
Introduction to Deep Learning and Neural Networks
Coursera
June 1, 2026 – Present
Introduction to TensorFlow for AI, ML, and Deep Learning
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying AI to diverse domains such as surveillance, medical diagnosis, and real estate, indicating adaptability and a broad perspective. The certifications in RAG and Agentic AI align well with current industry trends and the target role of an AI Engineer. The candidate's education in Computer Science (AI & ML) further reinforces a strong cultural fit for an AI-centric organization.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting strong problem-solving and analytical skills. The focus on reducing manual review time and optimizing response latency points to an operational mindset. However, without direct assessment data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively evaluated.