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Hire an LLM Engineer, Seattle
Enterprise LLM engineering in Seattle, for Microsoft partner ecosystem software, Amazon B2B tools, Boeing technical operations, requires infrastructure that consumer-grade implementations skip: prompt versioning with rollback, A/B testing pipelines that actually measure output quality, and multi-provider fallback that keeps features running during model provider outages.
At 10,000+ daily LLM calls, token cost is an engineering metric that belongs in your monitoring stack alongside latency and error rate. We build the full production layer, not just the model integration.
Pricing is scoped to your integration complexity and evaluation depth after a technical discovery call, not quoted off a rate card.
Tell us your current daily LLM call volume and the reliability problem you are solving.
Prompt versioning at enterprise scale means treating every prompt change like a code change: PR review, CI evaluation, and a rollback path if the change degrades production quality. A prompt stored in an environment variable has none of these properties. A prompt checked into Git as a versioned function, tested via CI, and deployed via a registry has all of them.
For Microsoft partner software or Amazon B2B tools where an LLM feature is customer-facing, a prompt regression is a product regression. The engineering requirement is the same as any other production code change: you need to know what changed, why it changed, whether it made things better or worse, and how to undo it.
A/B testing LLM outputs is fundamentally different from A/B testing UI changes. You cannot measure output quality with click-through rates. You need human evaluation at scale. The infrastructure for this: a shadow mode that runs both variants on live traffic without showing the results to users, a paired annotation queue that shows evaluators both outputs side-by-side, and statistical analysis that tells you when you have enough ratings to declare a winner.
At 10,000+ daily calls, model provider outages move from hypothetical to planning assumption. OpenAI, Anthropic, and Azure OpenAI each had multiple outages exceeding 30 minutes in the past year. Multi-provider fallback with a consistent output interface is an availability requirement for enterprise features, not a nice-to-have.
01
A prompt change ships and degrades quality. There is no version record, no way to identify which change caused it, and no quick rollback. Result: hours of debugging. Fix: Git-based prompt registry with evaluation CI.
02
Prompt changes are tested manually in the playground, not against the full production input distribution. Quality degrades quietly. Fix: golden datasets with automated evaluation on every change.
03
The entire LLM-powered feature goes down when the primary model provider has an incident. Fix: multi-provider abstraction layer with automated fallback and consistent output format.
04
A prompt change increases average output length by 40%. No one notices until the monthly bill is 50% higher. Fix: per-call token cost logging with p95 budget alarms.
Every engagement starts with a one-hour technical discovery call. We review your current implementation, your prompt storage approach, your evaluation process (or lack of one), and your observability setup. We ask to see the actual prompt, not a description of it.
The scope document we produce identifies exactly what is missing and what needs to be built: whether the priority is the prompt registry, the evaluation CI, the fallback layer, or the cost monitoring. We do not build everything at once if a phased approach makes more sense for your team.
For Boeing or Microsoft partner work with security requirements, we can scope a VPC-only deployment using Azure OpenAI or AWS Bedrock. No customer data leaves your infrastructure.
Describe your current setup: the feature, the call volume, what is not working reliably, and your primary technical concern. We will reply within one business day.