Rivac LabsML/LLM CI/CD Pipeline Engineering
Shipping a model or prompt change isn't the same as shipping a normal code change — the artifact is a weights file or a prompt template, the test suite is an eval set, and a regression can look like a plausible answer that's just quietly wrong. This service builds CI/CD pipelines purpose-built for that: model and prompt versions as first-class build artifacts, automated evals gating every promotion, and staged rollouts with canary or shadow traffic so a bad release affects a slice of users before it affects all of them. It's for teams shipping ML or LLM features on a real cadence who are currently promoting changes by hand and finding out about regressions from users. The outcome is a release process where shipping a model update is routine and boring, with rollback one click away instead of a fire drill.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Diagnose the current release process for models and prompts — how changes are versioned, tested, and pushed today.
- 2
Pilot an automated pipeline on one model or prompt-driven feature, wiring in eval gates and canary rollout first.
- 3
Review pipeline gates, rollback triggers, and promotion criteria with engineering to agree on what blocks a release.
- 4
Roll out the pipeline across remaining models and services, with staged rollout and rollback as the default deploy path.
What You Get
Deliverables from this engagement
- CI/CD pipeline wired for model and prompt-driven releases
- Automated eval gates blocking regressions before promotion
- Staged rollout (canary/shadow) and rollback configuration
- Documented release runbook for engineering to operate
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 ML/LLM CI/CD Pipeline Engineering