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AI Engineer with less than a year in LLM, RAG & Computer Vision.
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Assessing your cultural and operational fit
AI Developer with 10 months of internship experience building production-grade LLM-based Retrieval-Augmented Generation (RAG) pipelines, Agentic AI systems, and real-time computer vision applications using Python, LangChain, HuggingFace Transformers, and FastAPI. Hands-on expertise in open-source LLM deployment (Ollama, WhisperX), vector databases (FAISS, ChromaDB), Low-Rank Adaptation (LoRA) / Parameter-Efficient Fine-Tuning (PEFT), and end-to-end AI delivery via Docker and CI/CD. Certified AI Data Quality Analyst with a published international research paper.
Bharathiar University
Master of Computer Applications (MCA)
August 1, 2023 – June 30, 2025
Avinashilingam University
Bachelor of Science in Computer Science (B.Sc.) · Computer Science
August 1, 2020 – June 30, 2023
DotWorld Technologies Pvt. Ltd.
AI Developer Intern
February 1, 2026 – Present
Coimbatore, Tamil Nadu, India
Sabareeza Technologies
Project Intern
October 1, 2025 – January 1, 2026
Coimbatore, Tamil Nadu, India
AI Meeting Assistant - Chrome Extension + Agentic AI Pipeline
February 1, 2026 – June 1, 2026
• Chrome extension that triggers a bot injection into Google Meet — user pastes meeting link, bot joins live and captures audio via WebRTC on the backend; eliminated 100% manual note-taking effort. • End-to-end agentic pipeline: live audio capture → WhisperX transcription → Ollama (LLM) summarization → structured output with attendee list, keypoints, and action items. • Fully open-source stack (Ollama + WhisperX) — zero paid API dependency; designed for enterprise deployment with Microsoft Teams integration on roadmap.
RAG Document Intelligence + MCQ Generation System
February 1, 2026 – June 1, 2026
• Production Retrieval-Augmented Generation (RAG) pipeline — ingests PDFs, chunks by chapter/topic/subtopic, generates embeddings, indexes into FAISS; reduced manual processing time by 40%. • MCQ engine retrieves top-k chunks via RAG and generates contextually accurate multiple-choice questions using LLM inference; cut manual question creation effort by 60%. • Tracked all experiments and model versions using MLflow for reproducibility and performance monitoring.
Weapon Detection AI System - YOLOv11n + Cross-Platform Inference
October 1, 2025 – January 1, 2026
• Trained YOLOv11n on COCO + custom dataset (handgun, knife, fire, rifle) — achieving 85%+ mean Average Precision (mAP) on real-world test data. • Exported to ONNX; Python inference engine integrated into .NET C# live video application; PostgreSQL stores all detections with timestamps for full audit trail.
Sentiment Analysis - RoBERTa (ONNX Runtime) Natural Language Processing System
October 1, 2025 – January 1, 2026
• Real-time sentiment classification using RoBERTa model (ONNX runtime) from HuggingFace — achieving 92%+ accuracy on domain-specific text classification. • Served via FastAPI REST endpoint for low-latency inference; ONNX runtime enables cross-platform deployment without framework dependencies.
AI - Data Quality Analyst
National Skill Development Corporation (NSDC)
June 1, 2026 – Present
Data Analysis and Visualization with Power BI
Microsoft
June 1, 2026 – Present
"Plant Disease Prediction for Sugarcane"
International Conference on Diversity in Computational Excellence
January 1, 2025 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an AI Engineer role, particularly in environments that value practical application, open-source contributions, and end-to-end project ownership. The diversity of projects (RAG, Agentic AI, Computer Vision, NLP) showcases adaptability and a broad interest in AI domains. The focus on efficiency (PEFT, open-source stack) and deployment (Docker, CI/CD) aligns with modern MLOps practices. The candidate's current internship and previous project internship indicate a proactive and hands-on learning approach, which is beneficial for dynamic tech environments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving orientation and an ability to deliver tangible results (e.g., 'eliminated 100% manual note-taking effort', 'reduced manual processing time by 40%'). The focus on open-source tools and enterprise deployment suggests a practical, cost-aware approach. The detailed project descriptions also imply good communication skills in articulating technical achievements and impact. The candidate's academic background and certifications further support a structured learning and application approach.