# Anush Deokar

> Anush Deokar is a Full Stack + AI Engineer, founding AI engineer at Meridian. RAG pipelines, MCP servers, agents, and small-scale LLM experiments.

I'm a founding AI engineer at Meridian, where I build the core AI platform end to end: RAG pipelines and MCP servers underneath, multi-agent flows in the middle, and the chat and voice agents people actually talk to on top. Before that I spent years as a frontend and full-stack engineer, so I own the whole path, from raw data to the interface.

Outside work I run experiments and build side projects: taking ideas apart, testing them carefully, and writing up what I learn.

## Experience

### Founding AI Engineer, Meridian (Jun 2025 – Present)

Founding engineer on the AI platform behind Meridian's products, designing systems end to end, from ingestion and retrieval to multi-agent flows in production.

- Architected the RAG pipelines at the centre of Hermes, Meridian's core AI platform: ingestion, retrieval and grounding that every product agent builds on.
- Built the Atlas MCP server, a standalone multi-tenant MCP server that exposes lead and lead-journey data to any MCP-compatible agent, with JWT and service-account auth, tenant resolution shared with hermes-core, and role-aware actions.
- Designed and shipped multi-agent flows on Hermes: production chat agents that hold real conversations with customers, plus core contributions to the voice agent.
- Built agents that make decisions, not just replies: judging candidates in AI interviews and qualifying leads in conversation, with results delivered to partner systems in real time.

Stack: Python, TypeScript, LangGraph, MCP, RAG, Node.js

### Software Engineer, Trek Health (Mar 2024 – Present)

- Led core development on turning terabytes of complex healthcare data into accessible, actionable insights for clients.
- Built a responsive, pixel-perfect product UI on top of systems designed for large-scale data operations, through the product's growth from 0 to $2M+ ARR.

Stack: TypeScript, Next.js, Node.js, Snowflake, AWS

### Software Engineer, matrice.ai (Jul 2023 – Apr 2024)

- Led and maintained the main product's React + TypeScript frontend, and backend microservices for image annotation.
- Integrated ML models into the product and coordinated testing with the ML team.

Stack: React, TypeScript, Go, Node.js, AWS

## Education

B.Tech, Computer Science and Engineering, IIT Jodhpur (2023)

## Skills

Python, TypeScript, LangGraph, MCP, RAG, Node.js, Next.js, Snowflake, AWS, React, Go

## Projects

### Tiny Frontier: Cheap memory in small LLMs (current)

How do modern LLMs keep memory cheap? I'm rebuilding sliding-window attention and attention sinks from scratch, testing them with controlled multi-seed experiments, all on a $0 compute budget.

- **1.8e-4**: max logit difference loading Google's Gemma 3 270M into my own code, with identical greedy decoding
- **5.8×**: less KV cache at 4K tokens for a 5:1 local:global model that matches a hybrid beating full attention
- **32 / 32**: buckets where evicting an attention sink hurt, after one change to the training data switched the sink on

Code: https://github.com/AnushDeokar/tiny-frontier  
Write-up: https://www.anushdeokar.xyz/writing  
Stack: PyTorch, From scratch, Multi-seed experiments

### Typeform clone

Form builder with workspaces and publishing, server actions and zod validation. 100+ users.

Stack: Next.js 14, Drizzle, Clerk, Stripe  
Live: https://typeform-clone-three.vercel.app/signin  
Code: https://github.com/AnushDeokar/typeform-clone

### Csgrid

Blogging platform with cover uploads, tags, and search and filtering by tag.

Stack: Next.js, Clerk, Prisma, ReactQuill  
Live: https://csgrid.vercel.app/  
Code: https://github.com/AnushDeokar/Csgrid

## Writing: Tiny Frontier

Rebuilding the memory tricks of modern LLMs at tiny scale, one controlled experiment at a time.

- [Part 1: Shrinking the frontier: rebuilding modern LLM tricks from scratch](https://www.anushdeokar.xyz/writing/tiny-frontier/background.md): The question, the rules for fair comparisons, a 3.4M-parameter control model, and proving the code is right by matching Google's Gemma 3 270M.
- [Part 2: How small can attention windows get?](https://www.anushdeokar.xyz/writing/tiny-frontier/local-global-attention.md): Sliding windows, local:global layer patterns, and what they cost in quality versus what they save in KV-cache memory.
- [Part 3: Attention sinks and streaming](https://www.anushdeokar.xyz/writing/tiny-frontier/attention-sinks.md): Streaming 16× past the training length with a 64-token cache. The predicted collapse didn't happen, and one controlled run showed why: sinks come from the training data format.

## Links

- Website: https://www.anushdeokar.xyz
- GitHub: https://github.com/AnushDeokar
- LinkedIn: https://www.linkedin.com/in/anushdeokar/
- X: https://x.com/AnushDeokar
- Email: deokaranush@gmail.com