AI Automation Engineer specializing in building scalable AI systems, agentic workflows, RAG pipelines, and intelligent automation infrastructures. Experienced in developing autonomous AI agents, email automation engines, and production-ready ML solutions using modern AI tools and cloud platforms.
I am an AI Automation & AI Engineer with a strong foundation in Machine Learning, Deep Learning, NLP, and Agentic Workflows. I design and deploy intelligent systems that automate complex business processes and enhance operational efficiency.
My expertise includes building RAG systems, scalable automation pipelines, AI-powered outreach engines, and production-ready AI applications using tools like n8n, Make, Railway, and modern LLM frameworks.
A scalable AI outbound automation system built in n8n with multi-workflow orchestration, behavioral tracking, GPT-powered auto-replies, and intelligent stop-logic to maximize conversions while maintaining high email deliverability.
A fully automated WhatsApp bot capable of processing text, transcribing voice notes (Whisper API), and analyzing images (GPT-4o Vision). Integrated with Supabase Vector Store for RAG to provide context-aware responses.
An automated lead management pipeline that captures Vapi.ai voice call transcripts, uses conditional logic to generate instant Slack summaries, and logs high-intent leads into a Google Sheets CRM.
An intelligent price tracking system built in n8n that scrapes live product data, builds historical price logs in Google Sheets, and uses AI Agents to identify high-value deals with instant Slack notifications.
Automates TikTok posting from Airtable using Make.com and Zernio. Supports Video, Static Image, and Carousel posts with smart routing, scheduled posting every 15 minutes, and auto-sync back to Airtable.
A RAG app that retrieves Wikipedia content with FAISS and generates answers using Mistral-7B for concise natural responses.
SegFormer-based medical image segmentation with CBAM, KSCO, STFT, and STET modules, tested on ISIC-2018 & PH2 datasets.
CNN classifier on CIFAR-10 dataset with augmentation, deployed in Streamlit for real-time predictions.
Fault classification using current signals with KNN, SVM, Logistic Regression, and Naïve Bayes. PCA applied for dimensionality reduction.