Senior Machine Learning Engineer

Job Title:

Senior Machine Learning Engineer

Key Skills:

Python, SQL, PySpark, Machine Learning, Scikit-learn, PyTorch, XGBoost, TensorFlow, ML Pipelines, MLflow, Databricks, AWS, Azure, GCP, Kubernetes, MLOps

Experience:

5+ YOE

Location:

LATAM (Guatemala, Honduras, El Salvador, Nicaragua, Panama, Colombia, Perú, Mexico, Costa Rica, Brazil, Ecuador, Paraguay, Uruguay)

Modality:

Remote

Key Responsibilities

• Design, develop, and deploy scalable machine learning solutions in production environments.
• Build and optimize end-to-end ML pipelines, including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
• Develop distributed data processing workflows using PySpark and SQL to support large-scale ML applications.
• Collaborate with Data Scientists, Data Engineers, Product Teams, and business stakeholders to translate business requirements into ML solutions.
• Deploy and manage machine learning models across cloud platforms such as AWS, Azure, GCP, and Databricks.
• Implement MLOps best practices, including model versioning, experiment tracking, CI/CD, monitoring, and lifecycle management.
• Design and maintain containerized ML workloads leveraging Kubernetes for model serving, batch processing, and orchestration.
• Drive improvements in model performance, scalability, reliability, and operational efficiency.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).
• 5+ years of experience as a Machine Learning Engineer, focused on production-grade ML systems.
• Strong programming experience with Python, SQL, and PySpark for large-scale data processing.
• Hands-on experience with machine learning frameworks and libraries such as Scikit-learn, PyTorch, TensorFlow, and XGBoost.
• Proven expertise building and maintaining robust ML pipelines using MLflow or similar MLOps platforms.
• Experience deploying and managing machine learning solutions in cloud environments, including AWS, Azure, GCP, and Databricks.
• Strong understanding of the complete ML lifecycle, from data preparation to production monitoring and maintenance.
• Experience working with Kubernetes and containerized workloads for machine learning applications.
• Excellent communication skills with the ability to collaborate effectively across cross-functional teams.

Preferred Skills

• Experience with MLOps, CI/CD pipelines, and model governance frameworks.
• Knowledge of model monitoring, observability, and performance optimization techniques.
• Experience working in Agile development environments.
• Familiarity with Docker, workflow orchestration tools, and large-scale distributed computing platforms.
• Exposure to generative AI, LLMs, or advanced machine learning systems is a plus.