Remote Python ML Engineer: Build Scalable Production Models

BairesDev; Antioquia; Remote; COP 292,047,000 - 454,295,000; Full time

Benefits offered by this job

Remote work

Competitive pay in USD or local

Equipment provided

Flexible hours

Paid parental leave

Paid vacations

Global team

Mentorship and growth

Job summary

BairesDev is hiring a Machine Learning Engineer (Python) to design, train, and deploy ML models into production. You will build scalable, high-performance systems that translate research into business value.

Work 100% remotely with a global Top 1% tech talent network, flexible hours, and strong career development. You will collaborate with cross-functional teams to ensure models meet business goals and infrastructure requirements.

Qualifications

• 4+ years of experience in Machine Learning Engineering, Data Science, or Software Engineering.

Responsibilities

• Design and implement production-ready machine learning models and algorithms using Python.
• Build and optimize end-to-end ML pipelines, covering data preprocessing, feature engineering, and model training.
• Deploy models at scale using frameworks like PyTorch and TensorFlow within production environments.
• Manage and transform large datasets on platforms like Databricks to ensure data quality and reliability.
• Monitor model performance and accuracy in production, implementing retraining strategies to handle data drift.
• Collaborate with cross-functional teams to align model architectures with business goals and infrastructure requirements.

Skills

Machine learning

Python

Deep learning

Model deployment

Data processing

Tools

PyTorch

TensorFlow

Databricks

Job description

BairesDev is hiring a Machine Learning Engineer (Python) to design, train, and deploy ML models into production. You will build scalable, high-performance systems that translate research into business value.

Work 100% remotely with a global Top 1% tech talent network, flexible hours, and strong career development. You will collaborate with cross-functional teams to ensure models meet business goals and infrastructure requirements.