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AI Engineer with less than a year in Computer Vision, NLP & Multi-modal AI
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Fresher AI Engineer with hands-on experience building production-grade Computer Vision, NLP, and Multi-modal AI systems including a YOLOv8 defect detection pipeline at 92.6% accuracy and a full-stack RAG-powered web application. Proven ability to ship end-to-end AI solutions using Python, PyTorch, TensorFlow, Flask, and Hugging Face. Interned at Infosys Springboard and led a 60+ member technical club as Secretary. Passionate about building real-world GenAI and ML products that scale.
Kangeyam Institute of Technology
B.Tech · Artificial Intelligence and Data Science
August 1, 2022 – June 30, 2026
Thirumurugan Matric HSS
SSLC
June 1, 2020 – May 31, 2020
MN. Murugappa Chettiar Girls HSS
HSC
June 1, 2020 – May 31, 2022
Infosys Springboard
Artificial Intelligence Intern
November 1, 2025 – January 1, 2026
India
KNIME
Data Analytics Intern (Inplant Training)
August 1, 2024 – August 31, 2024
India
Fake News Detection System
June 1, 2026 – Present
Built a binary text classifier using TF-IDF and Logistic Regression achieving 96.4% accuracy; outperformed Naive Bayes and SVM baselines by 3-4% F1-score with 5× faster inference.
BridgeGuard AI: Intelligent Bridge Inspection System
June 1, 2026 – Present
Engineered a YOLOv8-based real-time object detection system classifying 4 bridge defect types (cracks, corrosion, spalling, rebar exposure) achieving 92.6% mAP accuracy on a custom-labelled dataset of 3,000+ images. Integrated ESP32-CAM and drone feeds with a live Flask dashboard at 25 fps; automated PDF inspection reports with geospatial heatmaps and severity scoring. Deployed on Hugging Face Spaces for public access and demonstration.
Multi-Modal AI Analysis System
June 1, 2026 – Present
Architected a cross-modal deep learning pipeline jointly processing image and text using CLIP embeddings; implemented cross-modal attention mechanisms improving classification F1-score by 11% over single-modality baseline. Designed modular encoder architecture allowing independent swapping of vision or language encoders without retraining the fusion layer.
View ProjectAI Mental Health Assistant
June 1, 2026 – Present
Built a conversational AI detecting user mood across 7 emotional states using a fine-tuned sentiment classifier (88% accuracy); routed users to context-appropriate responses via Transformer-based intent recognition.
Analytics Dashboards
June 1, 2026 – Present
Hotel Booking (Power BI/DAX): Executive dashboard tracking 12-month revenue and occupancy; DAX measures surfaced a 17% off-peak revenue gap, informing promotional strategy. Student Performance (Excel/Power Query): Automated ingestion of 500-row assessment data; dynamic KPI slicers for subject-wise faculty drill-down. Mental Health in Tech (Tableau): Visualized 1,200-respondent survey with KPI cards for burnout rate, anxiety prevalence, and work-life balance segmented by company size.
Artificial Intelligence
Infosys Springboard
June 1, 2026 – Present
Introduction to Python
Infosys Springboard
June 1, 2026 – Present
AI & MLOps
Udemy
June 1, 2026 – Present
Prompt Engineering
Infosys Springboard
June 1, 2026 – Present
Advanced Computer Networks
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse personal projects, ranging from bridge inspection to mental health assistants and fake news detection, indicate a broad interest in applying AI to various real-world problems. Their involvement in hackathons and club activities suggests a proactive and engaged approach to learning and community, which aligns well with a culture of innovation and continuous improvement. The target role of AI Engineer is well-aligned with their academic background and project experience, particularly in deep learning and model deployment. The breadth of skills across different AI domains and data analytics tools also points to adaptability.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and project ownership, as seen in the EcoPackAI project where they owned the project end-to-end. Their involvement in club activities (Cyber Champion Club, Association of AI & DS) suggests good organizational and leadership potential, as well as a collaborative spirit. The ability to automate workflows in KNIME indicates a problem-solving mindset and efficiency focus. However, without direct interview data, assessing stress handling or direct team collaboration in a professional setting is limited.