Noel Panicker is an Agent Engineer blending software engineering and applied AI to build agentic systems that bring reasoning, context, and automation into enterprise GTM workflows. He designs and scales pipelines that enable agents to research, plan, and act across data and systems, with hands-on experience in RAG, structured extraction, retrieval/evaluation pipelines, and agent frameworks. At Poggio Labs and Maison he focuses on productionizing interpretable, controllable agent behaviors; previously at Nexla he turned natural language and vision inputs into LLM-driven data transformation pipelines. His background spans research and industry—from Behavioral Transformers research at NYU to ML internships that sped up Q&A resolution and improved code-generation accuracy—demonstrating a rare mix of experimental rigor and product-minded engineering. Based in New York, he prioritizes modular, reliable systems that align model behavior with human intent. An understated strength is his ability to bridge deep-agent research with pragmatic orchestration work, shipping features that make complex AI workflows auditable and actionable.
1 year of coding experience
5 years of employment as a software developer
Bachelor of Technology - BTech Electronics and Communications Engineering , Bachelor of Technology - BTech Electronics and Communications Engineering at Delhi Technological University (Formerly DCE)
Master of Science - MS Computer Science, Master of Science - MS Computer Science at New York University
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Contributions:2 PRs, 1 issue in 3 months
agenticagentic-agiagentsaiautogen
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