Remote Machine Learning Engineer — Production ML & MLOps

BairesDev; Nuevo León; Remote; MXN 2,239,000 - 3,358,000; Full time

Benefits offered by this job

100% remote work

USD or local currency compensation

Hardware and software setup for home

Flexible hours

Paid parental leaves and holidays

Job summary

Machine Learning Engineer at BairesDev is responsible for building and deploying ML models into production. You will integrate data science and engineering principles to ensure scalable training, serving, and monitoring of ML systems across production environments.

You will design production-ready pipelines with Python and frameworks like TensorFlow or PyTorch, deploy model-serving infra, and implement MLOps to automate testing, deployment, and versioning.

Qualifications

• 4+ years of experience in Machine Learning Engineering or Software Engineering.
• Proven expertise in building and deploying ML models into production.
• Proficiency with Python and deep learning frameworks such as TensorFlow or PyTorch.

Responsibilities

• Design and develop production-ready machine learning pipelines using Python and frameworks like TensorFlow or PyTorch.
• Deploy and manage scalable model serving infrastructure to support real-time and batch predictions.
• Implement and optimize MLOps practices to automate model testing, deployment, and versioning.
• Monitor production models for performance degradation and data drift to ensure long-term reliability.
• Work closely with data scientists to translate experimental models into robust, maintainable production code.

Skills

Python programming

ML deployment

MLOps

Tools

TensorFlow

PyTorch

Model serving infra

Job description

Machine Learning Engineer at BairesDev is responsible for building and deploying ML models into production. You will integrate data science and engineering principles to ensure scalable training, serving, and monitoring of ML systems across production environments.

You will design production-ready pipelines with Python and frameworks like TensorFlow or PyTorch, deploy model-serving infra, and implement MLOps to automate testing, deployment, and versioning.