Rivac LabsAI Engineer
Turning a machine-learning idea into a feature that runs reliably in production is a different job from training a model in a notebook — it requires data pipelines, serving infrastructure, and monitoring that catches drift before your users do. We build the full path from data to deployed model: feature pipelines, model serving, and the integration work that makes an AI capability feel like a native part of your product rather than a bolted-on API call. This is for teams adding a genuine ML feature — recommendations, classification, personalization — not a general add-AI project without a defined outcome. You get a deployed, monitored model integrated into your actual product, with a clear owner for what happens when it starts drifting.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Diagnose the specific prediction or classification problem and what data is actually available to solve it.
- 2
Build and evaluate a pilot model against held-out data before committing to a production serving architecture.
- 3
Review model performance and failure cases with your team, including where the model should defer to a human.
- 4
Deploy to production with monitoring, retraining triggers, and rollback paths built in from the start.
What You Get
Deliverables from this engagement
- Deployed, monitored ML model integrated into your product
- Data pipeline feeding the model in production
- Evaluation report on model performance and known limitations
- Monitoring dashboard for drift and prediction quality
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about AI Engineer