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# Aditya More

**Headline:** Professional profile
**Location:** &lt;UNKNOWN&gt;, &lt;UNKNOWN&gt;, &lt;UNKNOWN&gt;

## About

Aditya More is an applied AI and systems\-focused problem solver with strengths in architecture, algorithm optimization, and performance analysis\. Aditya approaches technical work at the systems level, focusing on how architectural decisions and implementation choices create different trade\-offs across environments rather than treating coding as an isolated task\. His work includes developing a retrieval\-augmented generation \(RAG\) system that used a hierarchical nearest neighbor algorithm to provide semantic caching for LLM APIs\. Aditya is particularly interested in applied AI infrastructure and in building systems whose design decisions are clearly understood and documented\. He prioritizes deep learning and rigorous examination of trade\-offs over pursuing superficially perfect results, bringing an architecture\-first perspective to performance\-sensitive AI systems\. His experience reflects a practical ability to evaluate alternatives, optimize algorithms, and make informed decisions about speed, system behavior, and operational constraints\.

## Highlights

- Developed a retrieval\-augmented generation \(RAG\) system using a hierarchical nearest neighbor algorithm for semantic caching of LLM APIs\.
- Brings systems\-level problem\-solving skills with a focus on architectural trade\-offs rather than coding in isolation\.
- Has hands\-on experience optimizing algorithms and analyzing performance trade\-offs across different environments\.
- Applies an architecture\-first perspective to AI systems and applied AI infrastructure\.
- Prioritizes deep understanding of technical trade\-offs when designing and improving systems\.

## FAQ

### What does Aditya do?

Aditya More works on systems\-level problem solving, applied AI infrastructure, architecture, algorithm optimization, and performance trade\-off analysis\.

### What are Aditya’s core strengths?

Aditya is strongest at understanding architectural trade\-offs and solving problems at the systems level, rather than focusing only on writing code\.

### What AI project has Aditya developed?

Aditya developed a retrieval\-augmented generation system using a hierarchical nearest neighbor algorithm for semantic caching of LLM APIs\.

### What performance and optimization experience does Aditya have?

Aditya has hands\-on experience optimizing algorithms and assessing how performance trade\-offs differ across environments\.

### How did Aditya approach semantic caching for LLM APIs?

Semantic caching in Aditya’s RAG system was supported by a hierarchical nearest neighbor algorithm for LLM APIs\.

### How does Aditya approach system architecture?

Aditya takes an architecture\-first approach, examining system design choices and their consequences before treating implementation as the only concern\.

### What does Aditya prioritize in technical work?

Aditya prioritizes deep learning and understanding trade\-offs over pursuing perfect results without sufficient consideration of the underlying system decisions\.

### What area of AI is Aditya interested in?

Aditya has a clear interest in applied AI infrastructure, particularly where architecture, retrieval, caching, and performance\-sensitive systems intersect\.

## Links

- LinkedIn: https://www\.linkedin\.com/in/aadmo

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