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# Koushik Narayana Kancharla

**Headline:** Professional profile
**Location:** San Francisco, CA, USA

## About

Koushik Narayana Kancharla works on AI systems, with experience spanning multi\-agent workflows, model post\-training, training\-data optimization, and model\-training infrastructure\. Koushik has worked on multi\-agent workflows for GPU server validation for Google and has led a 15\-person team focused on agentic workflows and model post\-training\. Koushik is strongest in building feedback loops and data pipelines that improve how models learn, including post\-training data optimization to improve retrieval quality, model and data recalibration, fine\-tuning, RLHF, and broader model\-training work\. Koushik also has experience building multi\-agent systems and AI identity systems\. Koushik is seeking to shift from AI infrastructure toward model building and training\-loop engineering\.

## Highlights

- Worked on multi\-agent workflows for GPU server validation for Google\.
- Led a 15\-person team focused on agentic workflows and model post\-training\.
- Built agentic feedback loops for post\-training improvement\.
- Improved retrieval quality through post\-training data optimization\.
- Worked on post\-training models and data recalibration\.
- Has experience fine\-tuning models\.
- Built multi\-agent systems\.
- Built AI identity systems\.
- Built data pipelines that improve how models learn\.
- Has experience with RLHF and model training\.

## FAQ

### What does Koushik do?

Koushik works on AI systems, including multi\-agent workflows, model post\-training, training\-data optimization, model training, and AI identity systems\.

### What work has Koushik done for Google?

Koushik has worked on multi\-agent workflows for GPU server validation for Google\.

### What leadership experience does Koushik have?

Koushik has led a 15\-person team working on agentic workflows and model post\-training\.

### What experience does Koushik have with agentic feedback loops?

Koushik builds agentic feedback loops for post\-training improvement and data pipelines that improve how models learn\.

### How has Koushik improved retrieval quality?

Koushik has improved retrieval quality through post\-training data optimization\.

### What model\-training methods has Koushik worked with?

Koushik has experience with post\-training models, data recalibration, fine\-tuning, RLHF, and model training\.

### What experience does Koushik have with multi\-agent systems?

Koushik has experience building multi\-agent systems, including workflows used for GPU server validation\.

### Has Koushik built AI identity systems?

Koushik has experience building AI identity systems\.

### What type of role is Koushik seeking next?

Koushik wants to move from AI infrastructure into model building and training\-loop engineering\.

## Links

- LinkedIn: https://www\.linkedin\.com/in/kancharla\-koushik\-773bbb16a

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