AI Research & Engineering.
We place senior AI and ML engineers into product teams that are adding intelligence to their software — LLM features, RAG, ML pipelines, and MLOps.
Building an AI-native product? See our work with AI-native companies →
What we staff
LLM application engineers
Engineers who build production features on top of GPT-class models — prompt engineering, evaluation, RAG, and cost-aware architectures.
ML engineers
Model training and fine-tuning, feature engineering, and integrating trained models into product paths.
Data engineers
Pipelines, vector stores, embedding workflows, and the data plumbing AI features actually depend on.
MLOps engineers
Deployment, monitoring, retraining, and drift detection — the operational side of shipping AI to production.
Typical engagements
LLM & RAG integration
Adding a retrieval-augmented chat, copilot, or search feature to an existing product without breaking the surrounding UX.
ML pipeline build-out
Standing up the training, evaluation, and deployment pipeline behind a model that already works in a notebook.
AI features inside existing teams
Embedding an AI engineer into a product squad to ship intelligent features alongside your product engineers.
Ten days from brief to embedded
Same process as the rest of our staff augmentation work: a 30-minute brief, a shortlist within days, your interview process, and the engineer live in your standups by day 10. See our full staff augmentation process →
Common questions
What kind of AI engineers do you place?
LLM application engineers, ML engineers, data engineers, and MLOps engineers. Senior only, with production experience rather than notebook-only backgrounds.
How fast can someone start?
Same 10-day timeline as our other engagements: brief, shortlist, interviews, embedded. Very specialised roles (research-heavy ML) can take a little longer, and we tell you that up front.
Do you work on top of OpenAI, Anthropic, or open-source models?
All three. We match the model choice to the constraints — latency, cost, data residency, and quality.
Can you help with an existing product, or only greenfield?
Both. Most of what we ship is AI features integrated into an existing product rather than brand-new AI apps.
Who owns the models and code we build together?
You do. All work product and IP is assigned to you under a written agreement.