Senior ML Ops Engineer - Remote, End-to-End ML Architect

Medium; Caviahue; On-site; ARS 133,982,000 - 178,643,000; Full time

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 bridge data science and software engineering, evaluate cloud solutions, and deploy scalable models into production.

This hands-on role focuses on ML lifecycle health, cloud performance and cost-efficiency, and delivering high-quality, scalable solutions that drive business value.

Qualifications

• Bachelor’s degree in software engineering/computer science or proven equivalent experience in data engineering or ML roles.
• Proven hands-on experience implementing and deploying ML solutions in production.
• Advanced knowledge of Python, SQL and Spark.
• Strong knowledge in Docker, Git, Bash Scripting, FastAPI, Sagemaker Studio, Airflow and CloudFormation.
• Solid understanding of data architectures and complex systems integration.
• Demonstrated ability to troubleshoot and solve complex software system issues.
• Extensive experience with AWS and GCP.
• Must be a team player with excellent communication.

Responsibilities

• Architect and develop end-to-end machine learning solutions.
• Manage and automate the complete ML lifecycle from development to production.
• Collaborate with data engineers and data scientists to create scalable solutions.
• Evaluate cloud architectures for performance and cost-efficiency.
• Interact with stakeholders to understand business challenges and propose technical solutions.
• Mentor and advocate for new developments across teams.

Skills

Python

SQL

Spark

ML Deployment

Cloud platforms (AWS/GCP)

Team collaboration

Communication

Education

Bachelor’s degree in software engineering/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 bridge data science and software engineering, evaluate cloud solutions, and deploy scalable models into production.

This hands-on role focuses on ML lifecycle health, cloud performance and cost-efficiency, and delivering high-quality, scalable solutions that drive business value.