Hire an LLM Engineer

Hire an LLM engineer who treats prompts as production code.

Prompts written in a notebook are not production code. Production LLM integrations need versioning, evaluation, structured output parsing, and cost tracking. We build the full integration, not just the call to the API.

Fixed scope, fixed price, quoted after we understand the number of features, model complexity, and evaluation requirements.

How we work

Tell us about the LLM feature you need to build.

What you get.

Four things every LLM engineering engagement includes. None of them are optional.

Senior engineer, not a junior with an API key

You get someone who versions prompts, measures output quality, and knows when GPT-5 mini will do the job for one-tenth the cost of GPT-5.

Fixed project scope with deliverables defined upfront

Before work starts you know exactly what you're getting: a list of deliverables, a timeline, and a fixed price. No open-ended billing.

Code you own outright

IP transfers to you on final payment. No license fees to WayFind Labs, no vendor lock-in. Your team can maintain and extend the integration without us.

Post-launch support window included

Every engagement includes a post-launch window for bug fixes, prompt adjustments, and questions. You're not handed a codebase and abandoned.

What we build.

Every layer of a production LLM integration, from model selection to evaluation.

Model selection

We benchmark two or three candidate models against your specific task and latency requirements before writing a line of integration code. You get the recommendation with the numbers.

Prompt engineering

Structured, versioned prompts with explicit output formats. System prompt design, few-shot examples, and chain-of-thought patterns where they improve reliability.

Structured outputs

JSON schema validation, function calling, and typed response parsing so your application code doesn't have to handle free-text output.

Tool use

Function-calling layers that give the model access to your APIs and data sources, with input validation and error handling built in.

Cost optimization

Token counting, prompt compression, caching strategies, and model tier selection. We document the cost per call so you can budget accurately.

Evaluation

Automated tests for output quality, consistency, and regression. Before any prompt change ships, you know whether it improved or degraded performance.

Who this is for.

Product teams adding LLM features
You know what you want to build (a summarizer, a classifier, a Q&A feature, a generative form) but you need someone who has done this in production to own the implementation.
Teams struggling with prompt reliability
If your prompts work 80% of the time and you can't figure out why they fail the other 20%, the fix is usually structured outputs, better few-shot examples, or a cleaner system prompt, not a better model.
Companies paying too much for GPT-5 where mini would do
Many tasks that teams route to expensive frontier models can be handled by smaller, faster, cheaper models with the right prompting strategy. We benchmark before you commit.

Frequently asked questions.

What's the difference between an LLM engineer and an AI agent developer?: An LLM engineer focuses on integrating a model into a product: prompt design, structured outputs, model selection, cost optimization, and evaluation. An AI agent developer builds systems where the model takes autonomous actions, calling tools, maintaining state across steps, and completing multi-step goals. Many projects need both; we scope them as one engagement when they overlap.
How do you choose which model to use?: We benchmark two or three candidates against your actual task before writing integration code. We measure output quality, latency, and cost per call for each. You get a written recommendation with the numbers so the decision is yours. We don't have vendor relationships that bias the recommendation.
How do you handle prompt versioning?: Prompts are checked into version control as first-class code, not hardcoded strings in the application. Every change to a prompt goes through the evaluation suite before deployment. We document the testing process so your team can follow it after handoff.
How do you measure whether an LLM feature is working?: It depends on the task. For classification, we measure precision and recall against a labeled test set. For generation tasks, we use a combination of automated metrics (BLEU, ROUGE) and LLM-as-judge evaluation. For business-critical tasks we often build a lightweight human review workflow. We agree on the evaluation criteria before work starts.

Ready to scope your LLM integration?

Describe the feature you need to build and the model you're currently using (or considering). We'll reply within one business day with a rough scope and price range, no commitment required.

Hire an LLM engineer who treats prompts as production code.

Fixed scope, fixed price, quoted after we understand the number of features, model complexity, and evaluation requirements.

What you get.

Four things every LLM engineering engagement includes. None of them are optional.

Senior engineer, not a junior with an API key

You get someone who versions prompts, measures output quality, and knows when GPT-5 mini will do the job for one-tenth the cost of GPT-5.

Fixed project scope with deliverables defined upfront

Before work starts you know exactly what you're getting: a list of deliverables, a timeline, and a fixed price. No open-ended billing.

Code you own outright

IP transfers to you on final payment. No license fees to WayFind Labs, no vendor lock-in. Your team can maintain and extend the integration without us.

Post-launch support window included

Every engagement includes a post-launch window for bug fixes, prompt adjustments, and questions. You're not handed a codebase and abandoned.

What we build.

Every layer of a production LLM integration, from model selection to evaluation.

Model selection

We benchmark two or three candidate models against your specific task and latency requirements before writing a line of integration code. You get the recommendation with the numbers.

Prompt engineering

Structured, versioned prompts with explicit output formats. System prompt design, few-shot examples, and chain-of-thought patterns where they improve reliability.

Structured outputs

JSON schema validation, function calling, and typed response parsing so your application code doesn't have to handle free-text output.

Tool use

Function-calling layers that give the model access to your APIs and data sources, with input validation and error handling built in.

Cost optimization

Token counting, prompt compression, caching strategies, and model tier selection. We document the cost per call so you can budget accurately.

Evaluation

Automated tests for output quality, consistency, and regression. Before any prompt change ships, you know whether it improved or degraded performance.

Who this is for.

Product teams adding LLM features

You know what you want to build (a summarizer, a classifier, a Q&A feature, a generative form) but you need someone who has done this in production to own the implementation.

Teams struggling with prompt reliability

If your prompts work 80% of the time and you can't figure out why they fail the other 20%, the fix is usually structured outputs, better few-shot examples, or a cleaner system prompt, not a better model.

Companies paying too much for GPT-5 where mini would do

Many tasks that teams route to expensive frontier models can be handled by smaller, faster, cheaper models with the right prompting strategy. We benchmark before you commit.

Frequently asked questions.

What's the difference between an LLM engineer and an AI agent developer?

An LLM engineer focuses on integrating a model into a product: prompt design, structured outputs, model selection, cost optimization, and evaluation. An AI agent developer builds systems where the model takes autonomous actions, calling tools, maintaining state across steps, and completing multi-step goals. Many projects need both; we scope them as one engagement when they overlap.

How do you choose which model to use?

We benchmark two or three candidates against your actual task before writing integration code. We measure output quality, latency, and cost per call for each. You get a written recommendation with the numbers so the decision is yours. We don't have vendor relationships that bias the recommendation.

How do you handle prompt versioning?

Prompts are checked into version control as first-class code, not hardcoded strings in the application. Every change to a prompt goes through the evaluation suite before deployment. We document the testing process so your team can follow it after handoff.

How do you measure whether an LLM feature is working?

It depends on the task. For classification, we measure precision and recall against a labeled test set. For generation tasks, we use a combination of automated metrics (BLEU, ROUGE) and LLM-as-judge evaluation. For business-critical tasks we often build a lightweight human review workflow. We agree on the evaluation criteria before work starts.

Ready to scope your LLM integration?

Describe the feature you need to build and the model you're currently using (or considering). We'll reply within one business day with a rough scope and price range, no commitment required.