Engineering Trust: What Production AI Actually Requires
Most AI fails in production not because of the model, but because the surrounding system was never engineered. Trust comes from reliability, transparency, and safety by design.
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AUTHOR
Sunny Khanna
Vice President, Engineering
Sunny leads engineering at Plaxonic, where he and his teams build and operate production AI and cloud systems for enterprises. He works at the point where models meet reality: the observability, safety, and reliability engineering that decides whether AI can be trusted once it leaves the demo.
BIOGRAPHY
Sunny leads engineering at Plaxonic, where he and his teams build and operate production AI and cloud systems for enterprises. He works at the point where models meet reality: the observability, safety, and reliability engineering that decides whether AI can be trusted once it leaves the demo.
Areas of Expertise
Production AIMLOpsReliability EngineeringCloud Systems
At a Glance
Role
Vice President, Engineering
Published
1 perspective
Company
Plaxonic Technologies
PUBLISHED WORK