Hire senior MLOps engineers to run models in production

Senior MLOps engineers who build training pipelines, model serving, monitoring and GPU infrastructure, so your models ship and stay healthy.

By the Ryz Labs team · Updated October 2026

Hiring MLOps engineers through Ryz gets you senior Latin American engineers who make machine learning and LLM systems repeatable, observable and safe to change. They are top 1% of the candidates we interview, they embed in your platform or ML team, and they work within an hour of US time zones.

What our MLOps engineers work on

Our MLOps engineers build the platform under your models. They work with Kubernetes, Terraform, MLflow, Kubeflow, SageMaker, Azure ML and Vertex AI, plus serving tools such as KServe, BentoML, Triton and vLLM. Typical projects:

Training pipelines that run from versioned data to a registered model, with reproducible environments and lineage.
Model serving platforms with canary and shadow deployments, autoscaling and rollback.
LLM infrastructure: self-hosted open models on GPUs with vLLM, model gateways, prompt and response logging, and cost tracking.
Monitoring for data drift, prediction quality and latency, with alerts routed to the team that owns each model.
CI/CD for ML: tests for data, features and models, with automated evaluation gates before promotion.
GPU capacity management, spot instance strategies and cluster cost reporting.

Skills we vet for

Kubernetes and containers. GPU node pools, resource requests, operators and debugging pods that fail only under load.
Infrastructure as code. Terraform or Pulumi for repeatable ML environments across dev, staging and production.
Model lifecycle tooling. MLflow or Weights & Biases for tracking and registries, and clear promotion rules.
Pipeline orchestration. Kubeflow Pipelines, Airflow, Argo Workflows or SageMaker Pipelines, with caching and retries.
Serving performance. Batching, quantization, KV cache settings for LLMs, and choosing between Triton, vLLM and simpler servers.
Observability. Prometheus, Grafana and OpenTelemetry for system metrics, plus model-level monitoring with Evidently or similar tools.
Security and access. IAM boundaries, secrets management, private networking and audit trails for model changes.
Cost control. Measuring cost per prediction or per token and cutting it without hurting quality.

How we vet MLOps engineers

Recruiters look for engineers who have run ML systems in production and handled the incidents that come with them. Our in-house ARC system ranks the pipeline, then candidates complete structured NTRVSTA AI interviews on platform design and failure handling. Recruiters review every candidate before and after the interview and send you a curated shortlist. AI scores are advisory, and people make the final calls.

Sample interview topics

Design a promotion path from experiment to production for twenty models owned by four teams. What is automated, and what needs a human sign-off?
Your vLLM deployment handles normal traffic but time-to-first-token spikes at peak. What settings and capacity changes do you look at?
A model in production is quietly degrading, but labels arrive 30 days late. How do you monitor it in the meantime?
How would you make a training run reproducible six months later, including data, code, environment and random seeds?
GPU spend tripled last quarter. Walk through how you would attribute it and bring it down.

Ways to hire MLOps engineers

Option	Best for	Trade-offs
Freelance marketplace	A defined setup task, such as standing up MLflow	Platforms need ongoing ownership. Short engagements leave infrastructure few people understand.
Staffing or recruiting agency	Sourcing DevOps and platform profiles	MLOps sits between DevOps and ML, and keyword screens miss that overlap.
In-house recruiting	A permanent ML platform team	A narrow, in-demand skill set with long hiring cycles.
Ryz Labs staff augmentation	Adding MLOps capacity to an ML or platform team	You set architecture direction. Best when you have models that need a better path to production.
Ryz Labs AI pod team	A new AI system that needs infrastructure, models and application code together	A dedicated pod including platform, ML and backend engineers. Scoped up front as a team.

Ryz is not the right fit if you want to buy a proprietary MLOps platform rather than build on open tooling in your cloud, or if you need round-the-clock coverage from engineers in Europe and Asia.

Why hire MLOps engineers from Latin America

Platform work depends on fast feedback with the teams who use it. When a data scientist cannot get a model deployed, or an on-call engineer sees latency climbing, the MLOps engineer needs to be online. Working within an hour of US time zones, our engineers handle those moments during your business day and pair with your teams live.

Latin America has a deep pool of senior DevOps and cloud engineers, and many have moved into ML infrastructure as companies took models to production. They bring that operational habit of runbooks, alerts and postmortems to ML systems, and they work in English with your platform and security teams.

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FAQ

Is MLOps different from DevOps?

It builds on DevOps but adds data and model versioning, training pipelines, evaluation gates and model monitoring. We vet specifically for that ML layer, not only for general infrastructure skill.

Can your MLOps engineers support LLM workloads?

Yes. Many of our engineers run LLM gateways, self-hosted open models on GPUs and logging pipelines for prompts and responses, alongside classic ML serving.

How is pricing determined?

Custom quote, scoped per team. Before you sign, we share the plan, the price and the names of the people who would do the work.

How does the contract work, and what hours are covered?

You sign one contract with Ryz. Our engineers work with us as independent contractors, and we handle paying them. They work within an hour of US time zones and can join your on-call rotation during their working hours.

Questions we didn't answer? Email info@ryzlabs.com.