AI Engineering
Nearshore MLOps engineers, in your time zone
We field senior MLOps engineers who get models and AI systems deployed, monitored, governed, and cost-controlled in production.
The problem
Models that work in a notebook but break, drift, or run up cost in production.
What we do
Deployment, monitoring, pipelines, governance, and the operating model for production AI.
Clients typically see
- Models reach production reliably
FAQ
Frequently asked
Deployment or monitoring?
Both — deployment and monitoring, plus the pipelines and operating model that keep models healthy over time. A model that works on launch day but drifts unnoticed is the common failure we prevent.
Governance and cost?
Yes — governance to keep production models trustworthy and auditable, and AI FinOps so inference cost doesn't quietly outgrow the value. Both are what keep a model running instead of switched off when the bill arrives.
Which platforms?
We work in your stack rather than imposing ours — the major cloud ML platforms and CI/CD adapted for models. The operating model matters more than the specific tool.