Rust for AI Engineers: Build Fast Inference Runtimes, Agent Memory Layers, and Tool Servers in

Python is fast to write but slow to run. If you've ever watched an AI agent crawl through a vector search, waited on a GIL-locked inference loop, or debugged a memory spike in a LangChain pipeline, this book is for you. Rust for AI Engineers teaches you to build the performance-critical layers of AI systems in Rust - the parts where Python runs out of road. You'll learn Rust through the lens of real AI engineering problems: agent memory, tool servers, vector search, LLM API clients, and production observability. What you'll build: - A working memory system for AI agents - episodic, semantic, and working memory in safe, concurrent Rust - A MCP-compatible tool server with a typed registry and async dispatcher - A fast vector search engine from scratch, with HNSW indexing and Qdrant integration - A streaming LLM API client with retry logic and rate limiting - no Python tax - PromptWarden, a zero-dependency prompt injection detection library >Every code example compiles. Every crate is real and published. No toy projects. Who this book is for: Backend engineers tired of Python's performance ceilings, AI engineers who want to ship production-grade components, and Rust developers ready to apply the language to the fastest-growing engineering domain. Ashish Sharda is an engineering executive with 15+ years at Apple, Yahoo, Visa, and Salesforce. He teaches Rust on LinkedIn Learning and builds open-source AI tooling including PromptWarden, rapid-rs, and Vajra.

Pages: 124, Paperback, Independently published