Latha Ramamoorthy explains why technically capable LLMOps platforms still fail to earn adoption, and how developer experience, trust, and workflow fit turn infrastructure into daily practice.
About Latha Ramamoorthy
Latha Ramamoorthy is a Vice President and Product Portfolio Operations Manager within Core Engineering Solutions at JPMorgan Chase, an organization supporting more than 10,000 engineers. She leads work connecting platform strategy, engineering priorities, and portfolio execution, with experience spanning developer platforms, API modernization, and engineering governance. Her focus is on translating technical investments into measurable outcomes through developer experience, adoption, and effective operational practices. Beyond her professional role, Latha contributes to the technology community through conference speaking, mentoring on ADPList, and serving as communications lead for IEEE Chicago. Her current interests include AI platform adoption and helping teams build capabilities that developers understand, trust, and use in their everyday work.
What motivated you to join the AI Builders Global Conference?
Absolutely. I'm excited to connect with people who are building AI solutions and working through the challenges of putting them into practice. What drew me to this community is the opportunity to exchange honest lessons: what worked, what created friction, and what we would approach differently. I'm particularly interested in the intersection of technology, developer experience, and adoption, where a technically strong platform still needs to earn its place in someone's daily workflow.
What shaped your journey through technology and AI?
I currently lead product portfolio operations within Core Engineering Solutions at JPMorgan Chase, connecting platform strategy, engineering priorities, and execution within an organization supporting more than 10,000 engineers. My work spans developer platforms, API modernization, and engineering governance, with a focus on helping teams turn technical investments into measurable outcomes. That work has shaped my interest in AI. Building a capable platform is only part of the challenge. Helping developers understand its value, trust it, and integrate it into their daily work is what makes that capability useful. This connection between platform delivery and developer adoption is what inspires my focus today.
Why do technically successful LLMOps platforms struggle with adoption?
This topic captures a challenge I care deeply about: delivering a platform and achieving adoption are different accomplishments. A platform can meet its technical requirements while developers still struggle to get started, understand its value, or fit it into their existing workflows. Those barriers deserve the same attention as the architecture. My session explores LLMOps through that adoption lens, including onboarding, developer feedback, workflow fit, and measures of meaningful use. I want attendees to leave asking more than "Does our platform work?" They should also ask, "Can developers accomplish something valuable with it, and what makes them want to return?"
Who should attend the AI Builders Global Conference?
I would encourage AI engineers, platform and product leaders, technical program managers, and anyone responsible for moving AI from experimentation into everyday use to attend. The conference offers an opportunity to learn across disciplines. Engineers can gain insight into adoption challenges, while product and program leaders can better understand the technical decisions behind AI delivery. That shared understanding helps teams build solutions that are useful, usable, and sustainable.