Hire AI full-stack engineers who ship LLM features users actually trust

• You run a live SaaS product and want to add an in-app assistant or support copilot grounded in your own docs and database, wired into the app you already ship, not a separate chatbot bolted on the side.
• You are building an AI-native product from zero and need one engineer who owns the whole path: the frontend, the API, auth and billing, and the LLM workflow, so it ships as a real product instead of a demo.
• A data scientist proved something works in a notebook, and now you need it productionized: a real API, a streaming UI, retries, caching, and the error handling a notebook never had.
• An existing AI feature hallucinates, runs slow, or costs more than expected, and you want someone to add eval suites, tracing, prompt versioning, and cost controls so you can change it with confidence.

Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For AI full-stack developers, we look specifically at:

• Streaming done right: token responses streamed into a React UI over SSE, with cancellation, partial-failure states, and reconnection, not a blocking call hidden behind a spinner.
• RAG they can defend: how they chunk and embed, why they picked pgvector, Pinecone, or another store, and, the real signal, how they measure retrieval quality and cite sources instead of trusting top-k blindly.
• Structured-output discipline: JSON schema or tool and function calling, validation on every model response, and a concrete plan for when the model returns malformed or off-topic output.
• Evals as a habit: a regression set for prompts so a model or prompt change can be measured instead of eyeballed, plus tracing to see what the model actually did in production.
• Cost and latency instincts: caching, token budgeting, model fallbacks, timeout and rate-limit handling, and the judgment to know when a request does not need an LLM at all.