Remote Senior ML Ops Engineer - Production ML & Cloud

Yo Ai Labs; Neuquén; Híbrido; ARS 90.873.000 - 136.310.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Long-term contract

High visibility role

Influence technical strategy

Collaborative international team

Flexible environment

Descripción de la vacante

Coderio is seeking a Senior Machine Learning Ops Engineer to own the architecture, development, and automation of end-to-end ML solutions. You will bridge data science and software engineering, evaluate cloud options, and deploy scalable models in production.

You will work in a remote-first, international setting, focusing on health, performance, and cost efficiency of ML workloads while mentoring teams on best practices and technical adoption.

Formación

• Bachelor's degree in software engineering or computer science or equivalent experience.
• Hands-on experience deploying ML solutions in production.
• Strong Python, SQL and Spark skills.
• Experience with Docker, Git, Bash, FastAPI, Airflow and cloud tooling.
• Understanding of data architectures and system integration.
• Proactive problem solving and strong communication.

Responsabilidades

• Architect and develop end-to-end ML solutions.
• Manage and automate the ML lifecycle from dev to production.
• Collaborate with data engineers and scientists to scale solutions.
• Evaluate cloud architectures for performance and cost.
• Align with business needs and communicate technical concepts.

Conocimientos

Python

SQL

Spark

Educación

Bachelor's degree in software engineering/computer science or equivalent

Herramientas

Docker

Git

Bash Scripting

FastAPI

Sagemaker Studio

Airflow

CloudFormation

Descripción del empleo

Coderio is seeking a Senior Machine Learning Ops Engineer to own the architecture, development, and automation of end-to-end ML solutions. You will bridge data science and software engineering, evaluate cloud options, and deploy scalable models in production.

You will work in a remote-first, international setting, focusing on health, performance, and cost efficiency of ML workloads while mentoring teams on best practices and technical adoption.