The new hot job in AI: forward-deployed engineers (www.ft.com)

Forward-deployed engineers (FDEs) are emerging as one of the hottest roles in AI as companies move beyond prototype LLM demos to real-world product deployments. These hybrid engineers sit at the junction of research, product, and customer success: they scope use cases, customize and fine-tune models, integrate retrieval‑augmented generation (RAG) and vector databases into stacks, and shepherd projects from PoC to production. Employers — from startups to cloud vendors — prize FDEs for their ability to translate domain requirements into performant, secure, and cost-effective AI systems that deliver measurable business value. The role matters because productionizing AI requires deep, cross-disciplinary skills: prompt engineering and instruction tuning, model fine‑tuning and RLHF basics, embeddings and similarity search (Pinecone, Milvus, Weaviate), orchestration with LangChain/LlamaIndex, and infra work with Docker/Kubernetes and cloud APIs. FDEs also build monitoring, evaluation, and governance pipelines (latency/cost optimization, concept drift detection, privacy safeguards, differential privacy and secure data handling). For the AI/ML community, this trend signals growing demand for practitioners who combine ML engineering, systems knowledge, and customer-facing instincts — a shift that will shape hiring, tooling, and best practices around safe, maintainable model deployments.