Remote ML Engineer: Flexible Hours & Impact

BairesDev; Colombia; Presencial; COP 312.412.000 - 437.377.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote work 100%

Competitive USD salary

Hardware provided

Flexible hours

Paid parental leave & holidays

Global team & mentorship

Descripción de la vacante

BairesDev is seeking a Machine Learning Engineer to build and deploy ML models at scale in a 100% remote role. You will integrate data science and engineering principles to ensure efficient training, serving, and monitoring of production ML systems.

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

Formación

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

Responsabilidades

• 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.

Conocimientos

Python

ML engineering

MLOps

Model serving

Herramientas

TensorFlow

PyTorch

Descripción del empleo

BairesDev is seeking a Machine Learning Engineer to build and deploy ML models at scale in a 100% remote role. You will integrate data science and engineering principles to ensure efficient training, serving, and monitoring of production ML systems.

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