The services the models live inside: async APIs, task queues, webhook ingestion that survives retries, and data models that hold up past the demo.

Tool-using agents with termination guarantees, durable state and audit trails — and knowing when an orchestrated workflow beats a multi-agent system.

Retrieval that stays accurate on real corpora: chunking strategy, reranking, query rewriting, graph traversal for multi-hop questions, and evals that score retrieval separately from generation.

Drawing the boundaries before writing the code: service decomposition, event-driven flows, domain modelling, and the deterministic control flow that keeps an AI system debuggable under load.

Authorization that a model cannot talk its way past: role-based access as data, confirmation gates on irreversible actions, audit written before side effects, and PII that never leaves the network it belongs to.

Shipping it and then watching it: containerized services, Helm-packaged workloads, CI/CD that tests before it deploys, metrics, tracing and centralized logs.

Cutting inference cost and latency: self-hosted serving, quantization, batch-size and KV-cache tuning, semantic caching, and routing that escalates only when reasoning demands it.

Parameter-efficient training and preference optimization — applied after prompting, retrieval and routing have been exhausted, and measured on a held-out set.

Speech-to-speech and vision-language pipelines: transcription, intent classification, low-latency synthesis and human escalation paths — multilingual where it matters.

Enough frontend to ship the whole product rather than only the API: typed React, streaming responses in the browser, auth flows, file upload, and interfaces that read as designed rather than assembled.