Primary Responsibilities

• Design, build, and maintain enterprise-scale MLOps platforms to support the entire ML lifecycle.
• Implement and scale cloud-native MLOps solutions on AWS, Azure, or Google Cloud Platform (GCP).
• Conduct code reviews, perform refactoring, and ensure optimization of ML model pipelines.
• Containerize ML models and manage deployment, version control, and performance monitoring.
• Automate testing, validation, and continuous integration/continuous delivery (CI/CD) pipelines for ML models.
• Work closely with data scientists, data engineers, and architects to integrate MLOps best practices.
• Develop comprehensive documentation for workflows, tooling, and system architecture.
• Use Python, Go, Ruby, or Bash to develop automation scripts and manage infrastructure.
• Apply knowledge of Linux systems and machine learning frameworks like scikit-learn, Keras, PyTorch, and TensorFlow.
• Monitor model quality in production and help enforce governance and compliance standards.

Required Qualifications

• Proven experience in building MLOps platforms at enterprise scale.
• Strong knowledge and hands-on experience with cloud platforms (AWS, Azure, or GCP).
• In-depth experience in ML model deployment, containerization (Docker/Kubernetes), and versioning.
• Experience with model testing, validation frameworks, and test automation.
• Solid understanding of CI/CD for machine learning and ML system monitoring.
• Proficiency in Python, and experience with scripting languages such as Go, Ruby, or Bash.
• Familiarity with Linux environments and open-source ML frameworks (scikit-learn, Keras, TensorFlow, PyTorch).
• Strong collaboration skills and ability to communicate technical concepts effectively.
• Relevant MLOps certifications are highly desirable (e.g., Google Cloud Professional ML Engineer, AWS Machine Learning Specialty, etc.).

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