Description

Build and maintain ML infrastructure for scalable model deployment and monitoring.

Responsibilities

• Design and manage containerized ML workloads using Kubernetes and cloud platforms (AWS, GCP, Azure).
• Develop robust data pipelines and model serving solutions using Python and SQL.
• Implement MLOps best practices, including CI/CD for machine learning models.
• Optimize model performance through distributed computing and parallel programming techniques.
• Collaborate with data science teams to integrate models into production environments.

Required Skills

• 7+ years of professional experience in software engineering or MLOps.
• Strong proficiency in Python programming and SQL.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Expertise in container orchestration tools, specifically Kubernetes.
• Proven experience deploying and managing infrastructure on AWS, GCP, or Azure.
• Solid understanding of distributed computing and parallel programming concepts.
• Ability to troubleshoot complex system issues and optimize resource usage.

Preferred Skills

• Experience with MLOps tools like MLflow or Kubeflow.
• Knowledge of big data technologies such as Hadoop or Spark.
• Familiarity with model optimization techniques including pruning, quantization, or distillation.

Key Skills

Education

Not specified