Remote ML Engineer — Production ML & MLOps

BairesDev; United States; Remote; MXN 1,313,000 - 2,063,000; Full time

Benefits offered by this job

Remote work

Competitive pay (USD)

Home office setup

Flexible hours

Parental leave & holidays

Global collaboration

Job summary

BairesDev, a leader in tech projects with a global, remote-first team, is seeking a Machine Learning Engineer to design and deploy production ML models at scale. You will collaborate with data scientists and engineers to translate research into robust, maintainable production code.

You will build end-to-end ML pipelines in Python using TensorFlow or PyTorch, deploy scalable serving infrastructure, and apply MLOps practices to automate testing, deployment, and versioning.

Qualifications

• 4+ years of experience in Machine Learning Engineering or Software Engineering.
• Proven expertise in building and deploying machine learning models into production.
• Proficiency with Python and deep learning frameworks such as TensorFlow or PyTorch.
• Hands-on experience with MLOps practices and model serving infrastructures.

Responsibilities

• Design and develop production-ready ML 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

ML Engineer

Python

TensorFlow

PyTorch

MLOps

English

Tools

Docker

Kubernetes

Job description

BairesDev, a leader in tech projects with a global, remote-first team, is seeking a Machine Learning Engineer to design and deploy production ML models at scale. You will collaborate with data scientists and engineers to translate research into robust, maintainable production code.

You will build end-to-end ML pipelines in Python using TensorFlow or PyTorch, deploy scalable serving infrastructure, and apply MLOps practices to automate testing, deployment, and versioning.