Hire AI engineers who put models into production, not just notebooks

• You want to add an LLM feature to your product, a support assistant or document Q&A, and need someone to build a RAG pipeline that retrieves the right context, cites its sources, and is measured on an eval set before it ships to users.
• Your data scientists have working models in notebooks, but nothing runs in production. You need an engineer to wrap them in a serving layer, add versioning and drift monitoring, and set up retraining so predictions stay accurate over time.
• You have proprietary data and want to fine-tune or adapt an open model with LoRA instead of paying per token to a hosted API, and you need someone to build the training data, tuning loop, and evaluation to prove it beats the baseline.
• Your inference bill or latency is out of control, and you need someone to optimize serving with quantization, batching, caching, and right-sizing the model, without dropping the accuracy your users depend on.

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

• RAG that actually grounds: how they chunk and embed documents, choose and tune a vector store like pgvector, Pinecone, or Weaviate, rerank retrieved context, and cut hallucination with grounding and citations instead of longer prompts.
• Evaluation discipline: whether they build offline eval sets and track task metrics before and after each change, catch regressions across prompt and model versions, and can explain why a model got better, not just that it did.
• Production ML fundamentals: train and serve skew, data leakage, and drift; model versioning and rollback; and serving a PyTorch, scikit-learn, or XGBoost model reliably with tools like TorchServe, Triton, ONNX, or vLLM.
• Fine-tuning judgment: knowing when to fine-tune versus prompt versus RAG, LoRA and PEFT tradeoffs, dataset curation, and spotting overfitting instead of shipping a tuned model that never beat the baseline.
• Cost and latency awareness: token budgets, batching, caching, quantization, and choosing the smallest model that clears the bar, with real numbers on the accuracy tradeoff.