Remote ML Engineer - Build & Deploy Production Models

BairesDev; United States; Remote; MXN 1,676,000 - 2,421,000; Full time

Benefits offered by this job

Remote work

Competitive USD compensation

Home office setup provided

Flexible hours

Parental leave

Career growth

Job summary

BairesDev is hiring a Machine Learning Engineer to build and deploy production ML models. You will integrate data science and engineering to ensure scalable training, serving, and monitoring of ML systems remotely.

You'll design production pipelines with Python and TensorFlow/PyTorch, deploy scalable serving infra, and advance MLOps practices. Join a global, diverse team delivering impact from anywhere.

Qualifications

• 4+ years of experience in Machine Learning Engineering or Software Engineering.
• Proficiency with Python and deep learning frameworks such as TensorFlow or PyTorch.
• Hands-on experience with MLOps practices and model serving infrastructures.
• Strong background in training, serving, and monitoring large-scale ML systems.

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

ML Engineering

Python

MLOps

Model Serving

Data Drift Monitoring

Tools

TensorFlow

PyTorch

Docker

Kubernetes

Job description

BairesDev is hiring a Machine Learning Engineer to build and deploy production ML models. You will integrate data science and engineering to ensure scalable training, serving, and monitoring of ML systems remotely.

You'll design production pipelines with Python and TensorFlow/PyTorch, deploy scalable serving infra, and advance MLOps practices. Join a global, diverse team delivering impact from anywhere.