About the role
The MLOps Engineer role consists of running the platform your models live on — training infrastructure, deployment pipelines, monitoring, GPU economics. DevOps keeps software running; MLOps keeps models running, which adds a problem software doesn't have: models silently get worse. If you have models in production and no one owns drift, serving, and retraining, this is the missing role.
Monthly rate
$5,500–$8,000/mo
All-in: contract, benefits, equipment, IP
Experience
10+ years infra/platform
3+ in ML systems
Location
Latin America
Argentina · Colombia · Mexico · Chile
Timezone
Full US overlap
Fluent English, onboarded in one week
Core stack
AI tools, daily
Verticals seen
What they own — and what they don't
What they own
- Build CI/CD for models: versioned training pipelines, reproducible experiments, safe rollouts and rollbacks
- Own model serving: latency, autoscaling, GPU utilization and cost
- Monitor what DevOps tools don't see: prediction drift, data drift, silent quality decay
- Run the retraining loop so models stay current without manual heroics
- Manage LLM infrastructure where it applies: gateways, caching, token cost observability
What they don't — and who does instead
- Train the models — that's the ML Engineer; MLOps makes training repeatable and deployment boring
- Build product features on models — that's AI Engineering
- Own general cloud infrastructure and app CI/CD — that's DevOps, a sibling role
- Build the data warehouse — that's Data Engineering
Who hires this role, and for what
Teams whose models deploy by hand. The data scientist SSHes into a box and copies a pickle file. It works until the person leaves or the model quietly rots. MLOps industrializes it.
Companies scaling from 2 models to 20. Ad-hoc serving survives a couple of models. Past that, without shared infrastructure every new model adds permanent operational load.
Products with GPU bills that hurt. Serving costs grow with usage. Utilization, batching, and right-sizing GPU spend is a specialized job that pays for itself.
- 01
Model deployment pipeline. From notebook to production behind an API — versioned, tested, one-command rollback.
- 02
Drift and quality monitoring. Catching the model that silently degraded three weeks ago, before the business metrics do.
- 03
GPU and serving cost control. Utilization, batching, autoscaling — cutting serving spend without cutting quality.
- 04
ML platform build-out. The shared registry, feature store, and pipeline layer so every team stops rolling its own.
Work our engineers at this role have shipped
- End-to-end MLOps stack for a healthcare SaaS — model registry, CI/CD, canary rollouts, HIPAA-compliant logging
- GPU cluster + serving layer for a Series B AI-native product
- Drift monitoring and auto-retraining pipeline for a fintech risk model
Do you actually need an MLOps Engineer?
You do, if:
- Models reach production through manual steps someone has to remember
- Nobody would notice for weeks if a production model's quality dropped
- Each new model adds ongoing manual operational work
- GPU/inference spend is material and unowned
You probably don't, if:
- You have no models in production yet — an ML or AI Engineer gets you there first
- You run one stable model that rarely changes — your DevOps engineer can carry it with some guidance
- Your 'models' are all foundation-model API calls — you need AI Engineering discipline, not ML serving infra
Not sure which role fits? Tell us the problem instead of the title — we'll tell you what we'd actually staff, even if it's not this. If it is this: discovery call today, matched profiles in 48 hours, onboarded in a week.
Hire a Senior MLOps Engineer