Team extension

Hire AI engineers

Retrieval, integration and the evaluation work that separates a demo from something you can charge for.

What hiring an AI engineer here gets you

The terms, before the pitch.

Senior and mid+ AI engineers on your board, billed per engineer per month with a three-month minimum. Evidenced by a multilingual document platform with a custom retrieval architecture and ERP integration that cut manual processing by 87%.

The role is mostly not model work. On a production AI product the largest block of engineering is data — ingestion, cleaning, chunking, permissioning — followed by integrations, and only then the pipeline itself. An engineer who wants to spend the first month choosing a model and the last month discovering the corpus is unusable has optimised the wrong variable.

What an AI engineer covers here

Six things this role owns, rather than a list of technologies.

Retrieval pipelines

Ingestion, chunking, embedding and ranking. Chunking strategy is the highest-leverage knob in the whole system and the least discussed one.

Permission-aware retrieval

Enforcing at query time who is allowed to see which document — the requirement that rules out fine-tuning for most enterprise cases, since training data is available to everyone who reaches the model.

LLM integration

Provider abstraction, streaming, fallback and cost control, so swapping a model is days of work rather than a rewrite.

Evaluation harnesses

Scored test sets and regression detection, so a quality drop is a failed build instead of a support ticket three weeks later.

Agentic systems

Tool use, orchestration and human-in-the-loop checkpoints, built with the failure modes designed in rather than discovered.

Cost and latency work

Token budgets, caching and routing between models. A feature that is cheap at a hundred users can be the largest line item at ten thousand.

The stack we staff for

What these engineers work in day to day.

Models

OpenAIAnthropicGeminiLlamaMistral

Frameworks

LangChainLlamaIndexLangGraph

Retrieval

PineconeWeaviateQdrantpgvector

Engineering

PythonFastAPIGoDockerKubernetes

How the engagement works

Six steps from a role brief to an engineer on your board.

Define the role

The stack, the seniority, and what the first month has to produce. That last part is the one most role briefs leave out, and it is the one that makes a shortlist useful.

Meet the engineers

You interview, against your bar and your process. If we do not have the right person for the role we say so rather than putting forward the nearest one — a bad match costs you a quarter.

Contract and NDA

An NDA before any of your code or design material is shared. You hold 100% of the IP from day one. Work can start within 1–2 weeks of signing.

Onboard into your process

Your board, your repo, your standups, your review conventions. The target for week one is a small change landed in your pipeline, not an architecture proposal.

Run it monthly

Billed per engineer per month, $4,000–$7,000 by seniority and from $4,000. Three months minimum, then rolling — below three months onboarding eats most of the value and both sides know it.

Scale or stop

Add engineers as the work grows, or end it with notice. A team of two to eight plus a tech lead is the usual shape, with a QA engineer when the work needs one.

When this is the wrong choice

Outstaffing is the wrong shape for some of the work people bring to it, and finding that out in month two is expensive for everyone.

You need research, not engineering

Training a model from scratch or pushing a state-of-the-art benchmark is a research role. These engineers build products on models that exist.

Nobody can say what "good" looks like

Without a definition of a correct answer there is nothing to evaluate against, and quality becomes an opinion. Settling that is a week of work and it comes first.

The corpus does not contain the answer

No pipeline fixes missing information. Worth taking a genuine random sample of your documents — not the tidy examples — before scoping anything.

Common questions

The three that come up on nearly every first call about this role.

Only after retrieval has been built and measured, and usually the measurement shows it is unnecessary. Retrieval changes what a model knows; fine-tuning changes how it behaves. Most teams asking for fine-tuning have a knowledge problem, and fine-tuning it produces a model that is confidently out of date the day your data changes.
An NDA before anything is shared, then data isolation, access controls and encryption as standard. Where data cannot leave your boundary at all, the pipeline is designed self-hosted so nothing reaches a third-party API — that constraint changes the architecture, so it needs stating in the first conversation rather than the fourth.
That is the common shape, and it works best when the split is explicit: your team owns the source data and what it means, ours owns the retrieval and serving path. The failure mode is both sides assuming the other owns data quality, which surfaces about six weeks in as a mysterious drop in answer accuracy.

What this role has shipped

A delivery that evidences the work above.

Need an AI engineer on the board?

Tell us the role, the stack and when you need someone starting. We come back with who we can put on it and what it costs, in writing.

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