Senior ML Ops Engineer - Remote | Python & Cloud

Fullstack; Municipio de Rincón de los Sauces; Remote; ARS 167,234,000 - 258,453,000; Full time

Benefits offered by this job

Remote-first culture

International team

Growth opportunities

Flexible, fast-paced environment

Job summary

Coderio is seeking a Senior Machine Learning Ops Engineer to own the architecture, development, and automation of end-to-end ML solutions. You will operate within an established engineering team, bridging data science and software engineering to deploy scalable ML models in production.

This is a hands-on role focused on ML lifecycle health, cloud performance, and cost-efficiency, with opportunities to influence technical strategy in MLOps and data architectures.

Qualifications

• Bachelor's degree in software engineering, CS or equivalent.
• Hands-on experience deploying ML models to production.
• Strong Python, SQL, Spark expertise.
• Experience with modern cloud platforms (AWS, GCP).

Responsibilities

• Architect end-to-end ML solutions and automate ML lifecycle.
• Manage ML deployment, monitoring, and cost efficiency in cloud.
• Collaborate with data engineers/scientists to scale solutions.
• Evaluate cloud architectures and optimize performance.
• Mentor teams on ML & MLOps best practices.
• Bridge data science and software engineering across SDLC.

Skills

ML deployment

Python

SQL

Spark

Teamwork

Cloud concepts

Education

Bachelor's degree in software engineering or computer science

Tools

Docker

Git

Bash scripting

FastAPI

Sagemaker Studio

Airflow

CloudFormation

Job description

Coderio is seeking a Senior Machine Learning Ops Engineer to own the architecture, development, and automation of end-to-end ML solutions. You will operate within an established engineering team, bridging data science and software engineering to deploy scalable ML models in production.

This is a hands-on role focused on ML lifecycle health, cloud performance, and cost-efficiency, with opportunities to influence technical strategy in MLOps and data architectures.