ML Engineer - Hybrid/Remote, Impactful AI Solutions

Cuesta Partners; Bogotá; Híbrido; COP 90.000.000 - 180.000.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health Plan

Team engagement activities

Annual performance bonus

Growth opportunities

Flexible work (hybrid)

PTO & holidays

Descripción de la vacante

Cuesta Partners is seeking a Machine Learning Engineer in Latin America to work with our broad client base on AI-driven engagements, delivering robust models and mentoring teammates.

You will design, develop, and deploy ML solutions, build scalable pipelines, and collaborate across data science, data engineering, and MLOps teams to ensure reliable production systems in a hybrid work setting.

Formación

• 3-5 years of ML engineering experience.
• Bachelor's or Master’s in Computer Science, AI, Data Science, or related field.
• Proven experience designing, developing, and deploying ML models.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
• Strong understanding of algorithms, statistics, and ML techniques.
• Experience deploying models on AWS, GCP, or Azure.

Responsabilidades

• Design, develop, and deploy ML models for predictive analytics, classification, and clustering.
• Build and optimize scalable end-to-end ML pipelines from data preprocessing to deployment and retraining.
• Collaborate with data scientists, data engineers, and MLOps teams to ensure clean data and scalable infrastructure.
• Integrate ML models into production environments and monitor, retrain as needed.

Conocimientos

3-5 years experience

Python

ML libraries

Algorithms

Cloud platforms

Educación

Bachelor's or Master's in CS/AI/Data Science

Herramientas

TensorFlow

PyTorch

scikit-learn

Descripción del empleo

Cuesta Partners is seeking a Machine Learning Engineer in Latin America to work with our broad client base on AI-driven engagements, delivering robust models and mentoring teammates.

You will design, develop, and deploy ML solutions, build scalable pipelines, and collaborate across data science, data engineering, and MLOps teams to ensure reliable production systems in a hybrid work setting.