Nihar Shah

I’m a Computer Science undergraduate focused on building applied AI systems, especially agentic workflows, multi agent orchestration, and real-world AI applications. My work centers around taking AI beyond demos and turning it into reliable, production-like systems that solve practical problems. During my internship at Confido Health, I worked extensively on AI voice assistants where I designed prompt systems, evaluated real world call scenarios, and built testing workflows to improve reliability, edge case handling, and task completion. This experience helped me understand how AI systems behave outside controlled environments and how to make them robust. Outside of that, I actively build and experiment with agent-based systems and LLM driven workflows. I’ve worked with frameworks like OpenClaw to design orchestration pipelines, task delegation systems, and continuous execution loops. I also regularly experiment with new models, local LLM setups, and automation pipelines to understand what works in real use cases. I enjoy working in fast moving environments where I can take ownership, build end to end systems, and iterate quickly. I’m particularly interested in roles where I can work on AI infrastructure, agent systems, or applied AI products with strong engineering teams.