Model Infrastructure (MLOps)

Nuvnext; Región Centro; Hybrid; MXN 600,000 - 900,000; Full time

Job summary

Nuvnext in Guadalajara, Mexico seeks an engineer to run production services with monitoring and cost awareness. This in-person, full-time role focuses on tying model providers to reliable deployments and keeping costs under control.

You will deploy and update vision models at client sites, monitor latency and errors, and document what you run while collaborating closely with founders and product teams.

Qualifications

• You have run production services with monitoring.
• Fluent Python or TypeScript.
• You care about cost as much as about the result.
• You document what you run.
• Nice to have Containers and deployment on client hardware.
• Experience with model APIs.
• Inference optimization.

Responsibilities

• Run the routing between model providers and its fallback.
• Measure and bring down the cost per agent and per client.
• Deploy and update vision models at the client's site.
• Monitor latency, errors and usage, with alerts that are useful.

Skills

Production monitoring

Python

TypeScript

Cost optimization

Documentation

Tools

Containers

Model APIs

Job description

Engineering Guadalajara · in person Full time

The agents depend on models from different providers, and the vision models run at the client's site. You make all of that run reliably, with its cost measured and a plan for when a provider fails.

The role

• Run the routing between model providers and its fallback.
• Measure and bring down the cost per agent and per client.
• Deploy and update vision models at the client's site.
• Monitor latency, errors and usage, with alerts that are useful.

Your first 90 days

• Day 30

  You learn how the agents call the models and what it costs.

• Day 60

  You deliver a measured improvement in cost or stability.

• Day 90

  You own the deployment and monitoring of a client's models.

Stack and tools

What we look for

Must have

• You have run services in production with monitoring.
• Fluent Python or TypeScript.
• You care about cost as much as about the result.
• You document what you run.

Nice to have

• Containers and deployment on client hardware.
• Experience with model APIs.
• Inference optimization.

What working here is like

What you build goes into products that operate real organizations. You work directly with the founders, with no layers between your code and production, under the same rule we give the intelligence: a person signs what is critical.