AI Engineer
AI Engineer with a Computer Science degree from the Australian National University. I design, build, and deploy machine learning systems that solve real-world problems.
I'm Aryan Odugoudar — an AI Engineer and Computer Science graduate from the Australian National University. I specialise in building end-to-end machine learning pipelines, from data wrangling to production deployment.
My work sits at the intersection of deep learning, NLP, and computer vision. I'm passionate about turning cutting-edge research into reliable, scalable systems that create real impact.
Building production AI applications and intelligent automation workflows from concept to deployment.
Experimenting with modern deep learning, continual learning, and LLM-driven systems.
Shipping practical, scalable systems across web, cloud, and edge platforms.
Architected Copilot Studio and Power Automate agents for RFP response automation, developed LLM-powered OCR vehicle inspection apps, and built Flask + grounded search solutions for executive market intelligence.
Building Bash/Linux challenges to evaluate and train LLMs. Delivered 20+ high-quality challenge sets across multiple technical domains.
Developed a Generative AI model for Australian aged-care understanding, improving model performance to 89% accuracy over prior baselines.
Built an LLM-driven HR assistant for certification analysis, enabling recruitment teams with 95% accuracy insight support.
Guided 40+ students to build and deploy first ML models through practical workshops with university faculty and AWS trainers.
Built dietary prediction ML workflows from patient data and optimised model and data processes for significant speed and quality gains.
Conversational campus assistant with GPT-4o, Whisper, ElevenLabs, and real-time lip-sync avatar interaction.
CNN-based deep learning pipeline for classifying five arrhythmia classes with 98%+ accuracy.
Computer vision system for underwater waste detection and classification using advanced image processing.
Real-time ML malware detection with continual learning strategies for sustained adaptive performance.
Multi-portal app + web platform for tracking police deployment, crowd movement analysis, and coordination.
Web app tracking student coding progress from LeetCode & Codeforces with admin panel and class leaderboard.
Face-recognition web application using OpenCV and Python to detect and identify faces in uploaded or live image inputs.
IEEE National Level Hackathon (IC Hack)
Amazon National Level Hackathon (Alexathon)
CBSE State Level Science Exhibition
CBSE National Level Science Exhibition
Have an AI challenge or an idea that needs machine learning? Let's talk.
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