AI Systems Engineering: From Prototype to Production

Building production-grade Artificial Intelligence systems has fundamentally transitioned from an empirical research endeavor into a rigorous engineering discipline. AI Systems Engineering: From Prototype to Production bridges the gap between high-level algorithmic concepts and low-level production infrastructure.

​Designed for software engineers, ML engineers, and systems architects, this book provides a deep, code-first exploration of how to build, optimize, and scale modern AI applications under strict Service Level Agreements (SLAs).

​What You Will Learn:

• ​LLM Runtimes & Serving Engines: Master asynchronous execution loops, continuous batching, and high-performance serving architectures using vLLM, TensorRT-LLM, and TGI.
• ​Memory & Quantization Mechanics: Optimize hardware saturation through PagedAttention, FlashAttention-2, and FP8/INT4 KV-cache quantization.
• ​Advanced Retrieval & Vector Search: Build high-recall RAG pipelines using HNSW graph tuning, sparse-dense hybrid search, and Reciprocal Rank Fusion (RRF).
• ​Agentic Workflows & Multi-Agent Swarms: Architect graph-based state machines, Directed Cyclic Graphs (DCGs), self-reflection loops, and fault-tolerant agentic frameworks.
• ​Multimodal Systems & VLMs: Understand vision encoders, patch extraction, and multimodal projection layers bridging vision and LLM decoders.
• ​Production Observability & Security: Implement OpenTelemetry distributed tracing, the RAG Triad evaluation framework, prompt injection defense, and PII guardrails.

​Complete with self-contained Python production simulators, production blueprints, and mathematical foundations, this book equips you with the exact tools needed to deploy resilient, scalable AI systems at enterprise scale.