Is MLOps Harder Than DevOps?
Yes, MLOps is harder than DevOps for most engineers, because it covers everything DevOps does plus data and models. A DevOps engineer keeps code shipping and systems running. An MLOps engineer also versions data, retrains models and catches models that quietly get worse while every system looks healthy.
Are MLOps and DevOps the same?
No. MLOps applies DevOps practices to machine learning, so it adds data, models and retraining on top of the usual code pipeline.
| DevOps | MLOps | |
|---|---|---|
| Core goal | Ship software fast and keep it running | Get models into production and keep them accurate |
| What gets versioned | Code, containers, config | Code, datasets, features, model weights |
| Pipeline | Continuous integration and delivery (CI/CD) | CI/CD plus continuous training |
| How it fails | Errors and downtime you can see | The app stays up while predictions get worse |
| Monitors | Uptime, latency, CPU and memory, error rates | All of that, plus data drift and prediction quality |
| Typical tools | Terraform, Docker, Kubernetes, GitHub Actions, Prometheus | The DevOps stack plus MLflow, Kubeflow, Airflow, Feast, Evidently AI |
Why is MLOps harder than DevOps?
MLOps is harder because the system can break without anything looking broken.
- Same code, different results. In DevOps, the same code gives the same output every time. In MLOps, the same code trained on slightly different data can give a different model.
- Silent failures. A model can return answers with zero errors while its accuracy drops as real-world data changes. An MLOps engineer has to monitor for that drift, not just for downtime.
- A wider stack. On top of CI/CD, cloud and containers, MLOps engineers need to understand data pipelines, experiment tracking, model evaluation and retraining.
Is MLOps entry level?
Rarely. Most MLOps engineers move into the role from DevOps, platform engineering or machine learning engineering.
If you’re hiring, screen for both sides: real infrastructure work (CI/CD, Kubernetes, cloud) and at least one model they’ve deployed, monitored and retrained in production. Candidates who have only one side of that usually need time to grow into the role.
Do MLOps engineers earn more than DevOps engineers?
Yes, a little. In the US, MLOps engineers average about $135,000 a year against $130,000 for DevOps engineers. In Latin America the gap is wider: about $54,000 against $40,000.
| Salary per year | DevOps engineer | MLOps engineer |
|---|---|---|
| US average | $130,000 | $135,000 |
| LatAm average | $40,000 | $54,000 |
LatamCent salary estimates for remote roles with US companies, 2026. The MLOps engineers in our current candidate profiles earn $56,000 to $62,000 a year.
Should you hire an MLOps engineer or a DevOps engineer?
Hire a DevOps engineer if you don’t have machine learning models in production yet. Hire an MLOps engineer once models are live and need retraining, monitoring and governance.
- DevOps engineer if your priority is faster releases, reliable infrastructure and lower cloud costs for your core product.
- MLOps engineer if your data scientists or ML engineers spend more time deploying and fixing models than improving them, or if nobody notices when a model’s accuracy drops.
- Both? A strong DevOps engineer can run your first model or two. Once you have several models in production, a dedicated MLOps engineer usually pays for itself.
Models in production and nobody watching them?
LatamCent hires pre-vetted MLOps engineers in Latin America who work US hours and cost far less than a US hire. You interview a shortlist and make the hire in 21 days or less.
Hire an MLOps engineer
