Aishwarya Srinivasan is the co-founder of The Gen Academy, an AI education company she started with Arvind Narayanamurthy to teach professionals how to build practical AI systems. Her career spans applied machine learning at IBM and Google, startup advising at Microsoft, and developer relations at Fireworks AI.
After a Microsoft internship in 2016 and a master’s degree in data science from Columbia University, Srinivasan worked as a data scientist at IBM and Google. At Microsoft for Startups, she became a senior AI advisor working with the Y Combinator startup portfolio, helping founders navigate technical architecture, product development, and commercialization. She subsequently joined Fireworks AI as head of AI developer relations and growth, a transition she describes in her account of moving from data science into product-facing AI work.
At AI Engineer World’s Fair 2024, Srinivasan argued that startups need more than cloud credits: direct access to specialists, fast technical answers, opportunities to influence product teams, and support reaching customers. She described open-source application templates as a way to accelerate experimentation with sophisticated systems, including retrieval-augmented generation, without spending weeks assembling an initial prototype. Gabriela de Queiroz and Pamela Fox co-presented the workshop; Fox led its detailed implementation and search-evaluation demonstrations.
Srinivasan later co-founded The Gen Academy, where the Mastering Agentic AI program combines live instruction, weekly projects, and separate low-code and code-intensive tracks. Its curriculum includes context engineering, the Model Context Protocol, agentic systems, LangChain, and LangGraph. She describes the program as serving people with different technical backgrounds through hands-on implementation.
Her writing about Bria examines responsible generative AI in commercial visual-generation products, while her profile of Helfie.ai explores preventive-healthcare accessibility. More recently, her teaching has addressed how smaller teams can use organizational memory and AI agents to bring business context and accumulated decisions into everyday work.
