Karan Mungra | Profound

Karan's Profound AI Rep

Karan on the future cost and efficiency of AI infrastructure.

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Karan Mungra

Edges

Designing Resilient Systems

Karan designs backend systems that retain work and recover predictably when components fail. In Dhrishti, he combined tenant isolation, ready backup nodes, lifecycle checks, and failure queues. In the training-data pipeline, he used event-driven stages, dedicated failure queues, versioned processing, and targeted reruns. Across both systems, he made recovery an explicit part of the architecture rather than an operational afterthought.

Spotted in2 Stories

Building AI Infrastructure

Karan builds the systems around AI products, from data preparation and GPU-backed serving to agent orchestration, tools, and memory. He co-built a multi-terabyte training-data pipeline and designed distributed camera processing across GPU clusters. He also built a C++ memory service, worked on tool integration for Krutrim's assistant, and built the multi-agent orchestration layer that decomposed complex queries across six to eight specialized agents. His contribution sits at the boundary between backend engineering, model-serving needs, and production reliability.

Spotted in4 Stories

Evaluating AI Systems

Karan creates concrete checks for AI components that need measurable behavior. For the assistant memory system, he built an evaluation pipeline around production queries and expected memory behavior. For the voice-to-voice product at profound.me, he built a dedicated evaluation pipeline, used it to surface ASR misspelling issues, and drove iterative fine-tuning that resolved user-reported cases and approximately 95% of evaluation-identified cases by his recollection. In agent tooling and orchestration, he tracked tool-call success and fixed a recursive scheduling bug that caused runaway agent behavior. For the Dhrishti frame-skipping optimization, he validated quality by testing against historical data using the platform's hard metrics, confirming that approximate frame values produced the same outputs as full recomputation.

Spotted in4 Stories

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Karan Mungra

About Me

Karan is a backend engineer with experience building distributed systems and infrastructure for AI products. His work spans GPU-backed video processing, large-scale data ingestion, agent tooling, and memory systems.

As a Founding Engineer at profound.me, he is developing an AI Rep for professionals. More detail on his current technical charter and shipped work is still emerging.

At Krutrim, Karan architected tenant separation and failover for Dhrishti, a camera-processing platform that expanded from an internal deployment to use across multiple companies. The system preserved failed work in queues and moved camera processing to ready backup nodes.

He also co-built an event-driven training-data pipeline that processed 2 to 3 TB daily, with deduplication, versioned stages, failure queues, and reprocessing. For Krutrim's assistant, he built agent infrastructure and a C++ memory system, including a dedicated evaluation pipeline based on about 1,000 production queries.

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Karan Mungra

I think AI infrastructure will become more efficient and reduce the cost of inference.”

Karan Mungra

On AI infrastructure

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