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Senior Machine Learning Engineer

3 hours, 55 minutes ago
Colombia
Senior
Machine Learning Engineer
Software Development
Apache Airflow Apache Spark AWS Azure CI/CD Databricks Deep Learning Docker Feature Engineering Kubeflow 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 71

Description

  • Lead the design and implementation of scalable, production-grade machine learning systems in cloud environments.
  • Architect end-to-end ML solutions covering data ingestion, feature engineering, training, deployment, and monitoring.
  • Design and manage Docker- and Kubernetes-based workloads for training, batch inference, and real-time serving.
  • Oversee multi-terabyte data pipelines and ensure their reliability and performance.
  • Lead experimentation, A/B testing, model validation, and lifecycle management using MLflow and Databricks.
  • Drive automated retraining, model monitoring, bias mitigation, and performance optimization.
  • Evaluate and prototype emerging AI/ML technologies, frameworks, and architectures.
  • Collaborate with Product, Engineering, Data, and Leadership teams to deliver business-aligned ML initiatives.
  • Establish engineering standards, code quality practices, and technical documentation.
  • Mentor engineers and provide technical leadership across machine learning initiatives.

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 industry experience building, deploying, and scaling machine learning systems.
  • Advanced proficiency in Python, SQL, and PySpark for distributed data processing.
  • Experience with Scikit-learn, PyTorch, TensorFlow, and XGBoost.
  • Experience building production ML pipelines with MLflow or similar tools.
  • Experience deploying and operating ML solutions on AWS, Azure, GCP, or Databricks.
  • Strong understanding of complete ML lifecycles, including ingestion, training, evaluation, deployment, and monitoring.
  • Hands-on experience with Docker, Kubernetes, and containerized ML workloads.
  • Strong communication skills and ability to influence cross-functional teams.
  • Preferred: healthcare datasets, advanced degree, MLOps and CI/CD, deep learning for time series or sequential data, evaluation frameworks, Kubeflow, KServe, Airflow on Kubernetes, or fast-paced environments.

Interested in this position?

Apply directly on the company website

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