Reseña del libro "Forward Deployed Engineering (en Inglés)"
Stop building demos that die in the enterprise. Start shipping systems that actually get used.Most AI projects fail after the demo. The model works. The prototype looks great. Then it hits real customer data, messy workflows, security reviews, identity systems, rate limits, and the people who have to live with the result every day.Forward Deployed Engineering is the practical field guide for engineers who own that last mile.This is not another theory book about large language models. It is a hands-on playbook for the Forward Deployed Engineer (FDE)-the role hired by OpenAI, Anthropic, AWS, Palantir-inspired teams, and enterprise AI companies worldwide. Learn how to turn ambiguous customer requests into reliable, observable, secure production systems that deliver measurable business outcomes.What You Will MasterCustomer Discovery That Actually Works - Move beyond feature requests. Run technical discovery sessions, map real workflows, identify bottlenecks, surface constraints, and define accepted stakeholder success criteria.From Ambiguity to a Buildable Solution - Convert discovery artifacts into engineering requirements, choose the right first vertical slice, and clearly separate prototype, pilot, and production maturity.Enterprise Integration & Data Reality - Handle dirty data, identity resolution, schema drift, REST clients, webhooks, queues, retries, and legacy CRM/ERP architectures.Backend & Distributed Systems for the Field - Evolve scripts into resilient services. Master concurrency, timeout budgets, circuit breakers, caching, consistency models, and failure injection.Cloud, Containers & Safe Deployment - Implement Docker, Terraform, Kubernetes fundamentals, CI/CD evidence pipelines, and robust rollout strategies (rolling, blue/green, canary).Production LLM Applications & RAG - Master model selection, prompt/context architecture, structured outputs, hybrid retrieval, grounded generation, citation tracking, latency optimization, and streaming.Agents, Tools & MCP - Architect workflows, tool-using assistants, and bounded agents. Design safe tools, implement Model Context Protocol (MCP) servers, enforce human-in-the-loop approvals, and debug edge failures.Evaluation, Observability & Cost - Build golden evaluation datasets, combine deterministic and model-based review, enforce SLIs/SLOs, control spend, and monitor runtime behavior.Security, Identity & Governance - Establish trust boundaries, authentication, RBAC, resource-level policies, secret management, prompt injection defenses, data residency, and audit trails.Delivery, Adoption & Handoff - Manage scope control, pilots, launch criteria, incident leadership, runbooks, and transition ownership smoothly to customer teams.The final section breaks down a complete end-to-end engagement-Project Atlas-tracing the full lifecycle from an ambiguous customer brief through discovery, architecture, security review, pilot rollout, and operational handoff.Who This Book Is ForSoftware engineers transitioning into Forward Deployed, Solutions Architecture, or customer-embedded engineering roles.Backend and full-stack engineers tasked with deploying production AI inside enterprise infrastructure.Technical leads and architects responsible for LLM applications, autonomous agents, and RAG systems.Engineering leaders looking to close the gap between AI prototypes and durable business value.Turn discovery into APIs, agents, and production systems that actually work.
