Remote Senior ML Ops Engineer (Platform & Scale)

Turnkey Labs, Inc.; Neuquén; Remote; ARS 1,800,000 - 3,600,000; Full time

Job summary

Coderio is seeking a Senior Machine Learning 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 into production.

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

Qualifications

• Bachelor's degree in software engineering or computer science, or equivalent data/ML experience.
• Hands-on experience deploying ML models in production.
• Strong Python, SQL and Spark skills.
• Experience with Docker, Git, Bash, FastAPI, Sagemaker Studio, Airflow and CloudFormation.
• Solid understanding of data architectures and systems integration.
• Proven problem solving and debugging abilities.
• Experience with AWS and GCP cloud platforms.
• Team collaboration and clear communication skills.

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 scale solutions.
• Work on cloud solutions evaluating performance and cost efficiency.
• Interact with teams to understand business challenges and propose technical solutions.
• Communicate concepts clearly and mentor teams on new developments.

Skills

Python

SQL

Spark

ML deployment

Data architectures

System integration

Troubleshooting

AWS

GCP

Teamwork

Communication

Self-learning

Curiosity

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 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 into production.

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