I’m a Data Scientist at Prevalent AI, working on Generative AI–driven cybersecurity systems used in real enterprise environments. My work focuses on building LLM-based, RAG-powered, and agentic AI solutions that automate reasoning over cybersecurity knowledge graphs to deliver accurate, context-aware security insights. I specialize in LLMOps, AI workflow engineering, and Small Language Model (SLM) fine-tuning, with hands-on experience in PEFT, quantization, and prompt optimization to deploy efficient and cost-effective AI systems. I’ve also led initiatives around Agentic Data Fabric architectures, using remote AI agents and MCP to streamline data onboarding and automate security intelligence. As a mentor, I enjoy helping students and early professionals understand how AI is actually built and used in industry — beyond courses and tutorials. I focus on practical learning, system design thinking, and career clarity, especially for those aiming to grow in Data Science, Generative AI, or AI Engineering. I hold a Master’s in Artificial Intelligence and am passionate about building responsible, explainable, and production-ready AI systems. My goal is to help you learn the right skills, avoid common mistakes, and confidently move toward real-world AI roles.