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Senior Machine Learning Engineer

40 minutes ago
Mexico, Brazil, El Salvador, Guatemala, Honduras, Nicaragua, Panama, Paraguay, Peru, Colombia, Costa Rica, Uruguay, Ecuador
Senior
Machine Learning Engineer
DevOps and Infrastructure
Agile Apache Spark AWS Azure CI/CD Databricks Docker GCP Generative AI Kubernetes Machine Learning MLflow MLOps Python PyTorch Scikit-learn SQL TensorFlow XGBoost
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Coforge

Coforge

Coforge is a global digital services provider specializing in transforming businesses through technology and industry expertise, powering growth with innovative solutions and platforms.

IT Services
10K-50K
Founded 1992
View All Jobs 27

Description

  • Design, develop, and deploy scalable machine learning solutions in production.
  • Build and optimize end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
  • Develop distributed data-processing workflows using PySpark and SQL.
  • Collaborate with data scientists, engineers, product teams, and business stakeholders to translate requirements into ML solutions.
  • Deploy and manage models across AWS, Azure, GCP, and Databricks.
  • Implement MLOps practices including versioning, experiment tracking, CI/CD, monitoring, and lifecycle management.
  • Design and maintain Kubernetes-based containerized ML workloads for serving, batch processing, and orchestration.
  • Improve model performance, scalability, reliability, and operational efficiency.

Requirements

  • 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 skills in Python, SQL, and PySpark.
  • Experience with Scikit-learn, PyTorch, TensorFlow, and XGBoost.
  • Experience building and maintaining ML pipelines with MLflow or similar MLOps platforms.
  • Experience deploying ML solutions on AWS, Azure, GCP, and Databricks.
  • Strong understanding of the complete ML lifecycle, including production monitoring and maintenance.
  • Experience with Kubernetes and containerized ML workloads.
  • Knowledge of MLOps, CI/CD, model governance, monitoring, observability, and performance optimization.
  • Experience with Agile environments, Docker, workflow orchestration, distributed computing, generative AI, or LLMs is preferred.

Benefits

  • Remote work arrangement.
  • LATAM-based role open to candidates in Guatemala, Honduras, El Salvador, Nicaragua, Panama, Colombia, Peru, Mexico, Costa Rica, Brazil, Ecuador, Paraguay, and Uruguay.
  • Equal opportunity hiring based on skills without discrimination based on protected or personal characteristics.

Interested in this position?

Apply directly on the company website

Apply Now

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