Transforming complex data into intelligent action. Expert in building Multi-Agent Systems, RAG Architectures, and production-grade Generative AI solutions.
Global Kaggle Expert
Years Experience
Learners Mentored
A mix of Agentic AI, Generative AI, Computer Vision, and Classical ML solutions.
Sophisticated **Multi-Agent System** using **AutoGen** integrated with Model Context Protocol (MCP). Agents autonomously handle complex workflows: resume parsing, candidate enrichment, and recruiter Q&A. Reduced recruitment MTTR by 25%.
Scalable Retrieval-Augmented Generation system using **Pinecone** and **Weaviate** for vector storage. Implemented advanced chunking and BGE embeddings, boosting document Q&A accuracy by 35%.
Production deployment of fine-tuned YOLOv5 and Mask R-CNN models for industrial defect detection. Deployed using EC2 instance managing 1000+ parallel inference jobs.
High-accuracy data extraction system combining **OCR** and large LLMs (GPT-4/Claude) for invoice and claims processing. Achieved 80% operational efficiency gain.
Time-series forecasting model using **XGBoost/LightGBM** to predict short-term price movements in a simulated high-frequency trading environment. Optimized for latency.
Developed a swarm of specialized agents (Planner, Researcher, Editor, Critic) that collaborate to generate detailed, cited market research reports automatically. Utilized dynamic tool calling.
Implemented a fast Deep Convolutional Neural Network (DCNN) for real-time lane detection in self-driving car simulation environments, focusing on inference speed optimization.
Developed a zero-shot text classification API using pre-trained **Hugging Face Transformers** (BART/NLI models), enabling classification on unseen labels without re-training.
Built a complete ML pipeline for churn prediction using a **Scikit-learn** ensemble model. Deployed as a low-latency microservice via **Flask** with integrated CI/CD on Kubernetes.
Successfully fine-tuned the **Llama 3 8B** model for a specific industry compliance use case using **QLoRA (PEFT)**. Resulted in a 40% reduction in hallucination rate for compliance queries.
Led AI-powered data extraction and automation systems using GPT-4 and Claude. Built scalable ETL workflows reducing manual hours by hundreds/month.
Architected RAG pipelines (Pinecone + GPT-4o). Built MCP-integrated autonomous agents for recruiter workflows. Implemented ML observability stacks.
Developed conversational AI agents (Lex + LangChain) for 10k+ daily users. Established CI/CD pipelines reducing release cycles by 50%.
Trained **YOLOv5** and **Mask R-CNN** models (92% mAP) for Computer Vision solutions. Implemented MLOps practices for scalable deployment and integrated models with **MongoDB** and **BigQuery**.