Remote Senior ML Platform and MLOps Engineer

Semantix; Neuquén; Remote; ARS 1,200,000 - 2,000,000; Full time

Benefits offered by this job

Long‑term contract engagement

Strategic role in modern engineering

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 ML models into production.

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

Qualifications

• Bachelor's degree in software engineering or computer science or proven equivalent experience in data engineering or ML roles.
• Hands-on experience deploying ML solutions in production environments.
• Advanced knowledge of Python, SQL, and Spark.
• Strong knowledge of Docker, Git, Bash scripting, FastAPI, SageMaker Studio, Airflow, and CloudFormation.
• Extensive experience with AWS and GCP cloud platforms.
• Solid understanding of data architectures and systems integration.
• Ability to troubleshoot complex software systems and communicate clearly.

Responsibilities

• Architect and develop end-to-end ML solutions.
• Automate the ML lifecycle from development to production.
• Collaborate with data engineers/scientists to build scalable solutions.
• Evaluate cloud architectures for performance and cost-efficiency.
• Interact with teams to understand business challenges and propose solutions.
• Explain concepts clearly and train teams on new developments.

Skills

Python

SQL

Spark

Docker

Git

Bash

FastAPI

Sagemaker Studio

Airflow

CloudFormation

AWS

GCP

Education

Bachelor's degree

Tools

Docker

Git

Airflow

AWS

GCP

Sagemaker Studio

CloudFormation

FastAPI

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 ML models into production.

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