Staff augmentation
AI & Software Engineering
Engineers who build the product and the intelligence inside it — digital products, LLM integration and applied ML — embedded the same way our other roles are, inside your existing delivery process.
What it is
The Short Version
The same augmentation model, screened for a different discipline. These are product engineers who also ship AI-backed features into production systems: the application itself, plus the retrieval pipelines, LLM integrations, evaluation harnesses and the unglamorous plumbing that makes a model useful. They work in your repository alongside your existing team.
Who it is for
Product and engineering teams that need to ship AI-powered features without standing up an in-house AI hiring pipeline from scratch.
- You have an AI feature on the roadmap and no one free to build it
- You want AI capability inside your product, not a separate prototype
- Your team knows the domain but has not shipped LLM features before
- You need someone who treats evaluation and cost as first-class concerns
Capabilities
What That Covers In Practice
LLM integration
Wiring models into real product surfaces, with the error handling and fallbacks production requires.
Retrieval pipelines
Chunking, embedding and retrieval over your own content, tuned against real queries rather than demos.
Evaluation harnesses
Test sets and scoring so a prompt or model change can be measured instead of guessed at.
Applied ML
Classification, forecasting and recommendation work against your existing data.
Cost and latency tuning
Caching, model routing and prompt shaping, because an AI feature that is too slow or too expensive does not ship.
Data handling review
Working with your team on what data leaves your environment and what must not.
Questions
Things Clients Ask First
The engagement model is identical — scope, shortlist, you interview, embed. The difference is the specialism we screen for. These engineers have shipped AI features to production, not just built demos.
No. The point of this track is that the work lands in your codebase and your team can maintain it. A standalone AI system you cannot change is a liability.
We are deliberately provider-agnostic and work with whatever fits your constraints on cost, latency and data residency. That choice should follow your requirements rather than our preferences.
Data handling is agreed before any code is written, and your engineer works inside whatever boundaries your security and legal teams set. If data cannot leave your environment, that is a design constraint, not an obstacle.
You do. They work to your priorities, your process and your definition of done. We stay engaged on the commercial and retention side so that you are managing an engineer, not a supplier relationship.
Tell us early and we replace them. Our model only works if the placement lasts, so a bad fit is our problem to solve rather than something you are locked into.
Also from us
Other Services
- Microsoft Partner
AI Transformation
Take AI from stalled pilot to production on Azure and Microsoft 365 Copilot.
- Staff augmentation
Dedicated Engineering Teams
Extend your delivery capacity with experienced engineering professionals.
- Microsoft Partner
Microsoft Cloud & Business Applications
Modernise with Azure, Dynamics 365 and Power Platform.
Let us talk
Have An AI Feature Stuck On The Roadmap?
Tell us what you are trying to ship and the constraints around it. We will tell you honestly whether it needs an AI specialist or just an engineer.