I am a passionate Machine Learning Engineer with over 8 years of experience across fintech, trading, blockchain, web3, and e-commerce. My specialization lies in LLM-based pipelines, RAG, agentic workflows, and PEFT techniques like QLoRA. I have proven expertise in building scalable, production-grade MLOps pipelines using AWS, enhancing model performance, deployment, and monitoring, while also improving recommendation systems and ETL data pipelines. I have experience implementing Model Context Protocol (MCP) infrastructure to enable dynamic, real-time context sharing between models for more efficient, adaptive AI-driven solutions. My professional journey has allowed me to develop a robust skill set that includes designing and developing MLOps pipelines, integrating real-time data, and building ensemble forecasting systems. I am committed to leveraging my skills to contribute to innovative AI solutions.
Designed and developed a robust, scalable MLOps pipeline from data ingestion to model deployment using MLflow and Kubeflow on an AWS EKS cluster. Implemented RAG pipeline for a Crypto Trading AI Multi-Agent and integrated real-time data pipeline and building an ensemble forecasting system with LSTM, Prophet, and ARIMA.
Developed and maintained a vector search-based recommendation system on AWS using Elasticsearch, resulting in a 15% increase in customer engagement and a 12% increase in sales.
Designed and launched an e-commerce web application on AWS, integrating multiple third-party APIs to enhance product listings, payment processing, and customer support services.
Participated in the development of SaaS platforms, an ecosystem, and payment systems using Scrum-based Agile methodology with Test-Driven Development (TDD).
Master's Degree in Computer Science
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