I’m an AI Engineer focused on building end-to-end AI products and agentic workflows. My work centers on designing, implementing, and deploying LLM-powered systems, including RAG pipelines, tool-integrated agents, and real-time AI workflows, backed by strong foundations in predictive modeling, NLP, and experimentation. More recently, I’ve focused heavily on Generative AI and agentic systems. I’ve built AI workflows using LangChain and LangGraph, integrating LLMs with retrieval pipelines, vector databases, and external tools to support multi-step reasoning and conditional execution. My work includes designing RAG pipelines, prompt-driven agents, and tool-integrated systems for document understanding, customer support, and educational use cases. I’ve also used low-code platforms like n8n to prototype and automate complex AI workflows. At Innomatics Research Labs, I built and tuned predictive models using Python (scikit-learn) and automated sentiment classification with NLTK. I deployed interactive applications using Streamlit, tracked model versions via MLflow, and monitored performance metrics through Power BI dashboards. These efforts led to improved recommendation accuracy and faster model iteration cycles. As a Research Assistant at VIT, I applied artificial neural networks to optimize fuel blend emissions, achieving a high R² score and supporting academic publication. I also developed Python-based ETL workflows and created data visualizations that enhanced interpretation of experimental results. My core strengths include: • End-to-end AI systems design and deployment • LLM-powered products, agentic workflows, and RAG pipelines • Predictive modeling, NLP, and experimentation-driven development • Vector search, semantic retrieval, and tool-integrated reasoning I’m currently seeking full-time roles as an AI Engineer or AI Automation Engineer, where I can design and build AI-powered applications and intelligent workflows that automate processes, streamline business operations, and deliver measurable impact.…I’m an AI Engineer focused on building end-to-end AI products and agentic workflows. My work centers on designing, implementing, and deploying LLM-powered systems, including RAG pipelines, tool-integrated agents, and real-time AI workflows, backed by strong foundations in predictive modeling, NLP, and experimentation. More recently, I’ve focused heavily on Generative AI and agentic systems. I’ve built AI workflows using LangChain and LangGraph, integrating LLMs with retrieval pipelines, vector databases, and external tools to support multi-step reasoning and conditional execution. My work includes designing RAG pipelines, prompt-driven agents, and tool-integrated systems for document understanding, customer support, and educational use cases. I’ve also used low-code platforms like n8n to prototype and automate complex AI workflows. At Innomatics Research Labs, I built and tuned predictive models using Python (scikit-learn) and automated sentiment classification with NLTK. I deployed interactive applications using Streamlit, tracked model versions via MLflow, and monitored performance metrics through Power BI dashboards. These efforts led to improved recommendation accuracy and faster model iteration cycles. As a Research Assistant at VIT, I applied artificial neural networks to optimize fuel blend emissions, achieving a high R² score and supporting academic publication. I also developed Python-based ETL workflows and created data visualizations that enhanced interpretation of experimental results. My core strengths include: • End-to-end AI systems design and deployment • LLM-powered products, agentic workflows, and RAG pipelines • Predictive modeling, NLP, and experimentation-driven development • Vector search, semantic retrieval, and tool-integrated reasoning I’m currently seeking full-time roles as an AI Engineer or AI Automation Engineer, where I can design and build AI-powered applications and intelligent workflows that automate processes, streamline business operations, and deliver measurable impact.WWWW…