Models rarely fail on launch day. They fail quietly three months later when the data shifts and nobody is watching.
Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.
In short
The role
Skills
Process
Tell us the stack, the problem and how you want to work. We confirm fit on a short call.
We send profiles of pre-vetted senior engineers who match your stack and domain. You interview and choose.
Your engineers onboard and start delivering without a long recruiting cycle, with working software at the end of each sprint.
Engagement models
Add one or two senior engineers to your existing team to close a specific skill gap. Your leads, our specialists.
A small senior team working only on your roadmap, with a lead who owns delivery.
A scoped build such as a RAG system, a model or a pipeline, delivered to an agreed outcome.
Related work
Next step
Comparing options? Read what it costs to hire AI developers or contact us to discuss your project.
FAQ
An MLOps engineer builds the infrastructure and processes that let machine learning models be trained, tested, deployed and monitored reliably, much like DevOps does for software. For LLM systems this extends to prompt and model versioning, evaluation and inference cost control.
LLMOps applies MLOps practice to language model applications: versioning prompts and models, running evaluations on every change, tracing requests, and managing latency and cost in production.
Yes. We design and run on-premises and private cloud AI infrastructure, including GPU serving for open models, when data or cost requires it.
Yes. Our engineers work across AWS, Azure and Google Cloud and fit into your existing CI/CD and security practices.
Add an engineer to your team, take a dedicated team, or hand us a defined platform project. You interview the shortlist before anyone starts.
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Indicative ranges only get you so far. Tell us the specifics and get a scope and a fixed price in two business days.