Senior ML Ops Engineer - Remote & High-Impact

Felix; Municipio de Rincón de los Sauces; Remote; ARS 136,310,000 - 227,183,000; Full time

Benefits offered by this job

Long-term contract

High visibility role

Influence technical strategy

International team

Flexible environment

Job summary

Coderio is a remote-first company delivering scalable digital solutions for global clients. We seek a Senior ML Ops Engineer to own 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 to production, ensuring performance and cost-efficiency. This hands-on role emphasizes technical health of the ML lifecycle, collaboration with engineers, and mentoring teams to adopt new

Qualifications

• Bachelor's degree or equivalent in software engineering or computer science.
• Hands-on experience deploying ML solutions in production.
• Strong skills in Python, SQL and Spark.
• Experience with cloud platforms like AWS and GCP is required.

Responsibilities

• Architect and develop end-to-end ML solutions.
• Automate the ML lifecycle and production transitions.
• Collaborate with data engineers and data scientists for scalable solutions.
• Evaluate cloud options and optimize for performance and cost.
• Explain technical concepts clearly and mentor teams.
• Align technical work with business goals and stakeholders.

Skills

Python

SQL

Spark

Education

Bachelor's degree in software engineering or computer science

Tools

Docker

Git

Bash scripting

FastAPI

Sagemaker Studio

Airflow

CloudFormation

Job description

Coderio is a remote-first company delivering scalable digital solutions for global clients. We seek a Senior ML Ops Engineer to own 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 to production, ensuring performance and cost-efficiency. This hands-on role emphasizes technical health of the ML lifecycle, collaboration with engineers, and mentoring teams to adopt new