Remote ML Engineer: Production-Ready AI Systems

BairesDev; Mexico; On-site; PHP 5,474,000 - 9,124,000; Full time

Benefits offered by this job

100% remote work

Competitive USD salary

Home office setup provided

Flexible hours

Paid parental leave and holidays

Mentorship and growth opportunities

Job summary

BairesDev is seeking a Software Engineer specializing in Machine Learning to deploy and operate ML models in production, uniting data science and production engineering for automated training pipelines and high-performance serving layers.

You will work on scalable ML infrastructure, experiment tracking, and integration with production software, all while collaborating with a global Top 1% tech talent pool in a fully remote setting.

Qualifications

• 4+ years of experience in Software Engineering with a focus on Machine Learning or MLOps.
• Proven expertise deploying and operating ML models in production.
• Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.
• Hands-on experience with ML lifecycle tools such as MLflow, SageMaker, or Vertex AI.
• Solid understanding of containerization using Docker to ensure model reliability.
• Advanced proficiency in English.

Responsibilities

• Design and implement automated machine learning pipelines for model training, evaluation, and deployment.
• Build and maintain scalable model serving infrastructure using AWS SageMaker or GCP Vertex AI.
• Track experiments and manage model versioning using tools like MLflow or Weights & Biases.
• Containerize ML workloads with Docker to ensure portability across diverse environments.
• Optimize model performance and serving latency to ensure system reliability and observability.
• Integrate machine learning models into production software systems through robust software engineering practices.

Skills

Python

English

ML concepts

Tools

Docker

MLflow

Weights & Biases

SageMaker

Vertex AI

Job description

BairesDev is seeking a Software Engineer specializing in Machine Learning to deploy and operate ML models in production, uniting data science and production engineering for automated training pipelines and high-performance serving layers.

You will work on scalable ML infrastructure, experiment tracking, and integration with production software, all while collaborating with a global Top 1% tech talent pool in a fully remote setting.