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Hire an LLM Engineer, San Francisco
San Francisco product teams know how to call the OpenAI API. What they run into at scale is different: prompts that regress when inputs change, no systematic way to benchmark GPT-5 against Claude 3.5 for their specific task, and model costs that grow faster than revenue.
Production LLM engineering means treating prompts as versioned code, building evaluation pipelines, and making model selection decisions with benchmark data rather than intuition. We build the infrastructure that keeps LLM features working as your product scales.
Pricing is fixed per engagement and scoped to your feature complexity and evaluation depth.
Describe the LLM feature that is not working reliably in production.
A prompt that gets 90% accuracy in your dev environment will often drop to 75% in production. The cause is almost always input distribution shift: your test samples represent what you expected users to send, not what they actually send. Production inputs include typos, multi-language strings, unusually long inputs, and adversarial patterns your test set never covered.
The fix is a golden dataset with 100 or more labeled examples drawn from real production traffic, and a regression CI that runs the full evaluation suite on every prompt change before it ships. Without this, every prompt change is a gamble.
Model selection is an ongoing engineering decision, not a one-time call. GPT-5, Claude Sonnet 5, Gemini 2.5 Pro, and Mistral Large perform differently across task types. For classification tasks with a fixed label set, a fine-tuned Mistral 7B can outperform GPT-5 at 1/50th the token cost. The only way to know is to benchmark against your task.
At production scale, token cost is an engineering metric. We build cost dashboards that show cost per feature, per user, and per request type, with alerts when a prompt change causes a cost spike. This is the difference between an LLM feature that is economically viable and one that quietly erodes margin.
These are the specific problems that come up when LLM features hit real production traffic.
Prompts tuned against dev samples break on production inputs. We build regression CI with 100+ example golden datasets that run on every change.
Most teams pick GPT-5 because it is well-known. We benchmark 2-3 candidates against your actual task and present the cost-accuracy tradeoff.
LLMs that return prose instead of JSON crash application code. We implement structured outputs using function calling or Anthropic tool use.
Semantic caching cuts costs 30-60% on workloads with repeated queries. We implement GPTCache or Redis vector caching based on your latency requirements.
Prompts stored in env variables or hardcoded strings have no rollback path. We version prompts in Git and deploy via a prompt registry.
Provider outages cause feature outages. We build multi-provider fallback across OpenAI, Anthropic, and Mistral with normalized output format.
Every production LLM integration we build includes an evaluation layer. For classification tasks, that means precision and recall against a labeled test set. For generation tasks, we use ROUGE, BLEU, and LLM-as-judge evaluation with a small sample of human ratings to calibrate the automated scores.
Observability covers three dimensions: cost per call tracked by feature and user tier, latency at p50, p95, and p99, and output quality scores computed on a rolling sample. When any of these drift outside baseline, an alert fires before you see it in customer complaints.
We build this on top of Langfuse for tracing or Helicone for cost tracking, depending on what fits your stack. The dashboards are handed off to your team, not retained by us.
Describe the feature, the model you are using, and the specific reliability or cost problem you are running into. We will reply within one business day with a scope.