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 Retrieval-Augmented Generation (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.
An AI-powered Streamlit chatbot for querying Microsoft, Tesla, and Apple financials (2022–2024) with regression forecasts till 2034. Supports natural queries via Flan-T5.
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.