Generative AI Engineer | Expert in Fine-Tuning LLMs | Multi-Agent Systems & Agents | Agentic RAG & SQL | AI Integration Specialist | LangChain, LangGraph, N8N. VectorStore etc

As a Generative AI Engineer with a robust background in data engineering, I specialize in designing and deploying advanced AI solutions that drive innovation and deliver real-world impact. My journey has been defined by a hands-on approach to solving complex challenges, transitioning seamlessly from data pipelines to cutting-edge AI implementations.

Key Expertise & Achievements:

Fine-Tuning Vision Models: Successfully fine-tuned models like LLAVA and Qwen2-VL for diverse production use cases, including car damage detection, traffic analysis, and visual data extraction. These solutions have been deployed to enhance operational efficiency and enable data-driven insights in real-time environments.
Multi-Agent Systems: Developed and implemented multi-agent frameworks for Retrieval-Augmented Generation (RAG) and SQL agents, enabling dynamic and efficient data retrieval and analysis for complex workflows.
Integration with Legacy Systems: Engineered seamless integrations of generative AI solutions into legacy systems, ensuring minimal disruption while unlocking new capabilities for business transformation.
Agentic Automation: Leveraged tools like LangChain, LangGraph, and Python to design scalable, modular, and automated AI workflows, including advanced Vision-OCR systems for extracting actionable insights from unstructured data.
Notable Projects:

Designed a comprehensive AI-powered car damage detection system, delivering accurate assessments for insurance and automotive sectors.
Deployed traffic analysis solutions using fine-tuned vision models, supporting urban planning and real-time monitoring.
Built a multi-agent system for RAG, streamlining information retrieval for large-scale enterprise use cases.
I am passionate about applying generative AI to solve industry challenges, whether through automating processes, enabling data-driven decision-making, or scaling innovative solutions across domains.