Lead ML Operations Engineer (MLOps)

April 13

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• Technical Leadership: Lead and mentor a team of ML Operations Engineers, providing guidance, direction, and support in driving MLOps innovation and execution. • Infrastructure Design: Design and implement scalable and reliable infrastructure for deploying and serving machine learning models, leveraging cloud platforms and containerization technologies. • Model Deployment: Develop automated pipelines for deploying machine learning models into production environments, ensuring consistency, reliability, and reproducibility. • Monitoring and Alerting: Implement monitoring and alerting systems to track model performance, data drift, and other metrics, enabling proactive detection and mitigation of issues. • Model Versioning and Management: Establish version control and management processes for machine learning models, enabling easy tracking, rollback, and experimentation. • Continuous Integration/Continuous Deployment (CI/CD): Implement CI/CD pipelines for automating model training, testing, and deployment, reducing time to market and improving agility. • Scalability and Efficiency: Optimize the performance and scalability of machine learning infrastructure, leveraging techniques such as distributed computing, parallelization, and resource management. • Security and Compliance: Ensure machine learning systems comply with security and privacy standards, implementing access controls, encryption, and other security measures as needed. • Documentation and Best Practices: Document MLOps processes, best practices, and standards, providing guidance and training to data scientists and engineers. • Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and DevOps teams, to streamline the machine learning lifecycle and drive continuous improvement. • Research and Innovation: Stay informed about the latest advancements in MLOps tools and technologies, exploring innovative approaches and techniques to enhance machine learning operations.


• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field. • 7+ years of experience in software engineering, DevOps, or related roles, with a focus on building and maintaining infrastructure for machine learning operations. • Leadership experience, with a demonstrated ability to lead and mentor a team of engineers. • Strong understanding of machine learning concepts and techniques, with experience working with data science teams and machine learning models. • Proficiency in programming languages such as Python, Java, or Scala, and experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Experience with containerization technologies such as Docker and orchestration tools such as Kubernetes. • Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or MLflow. • Experience with CI/CD pipelines, version control systems, and automation tools such as Jenkins, GitLab, or CircleCI. • Strong problem-solving skills and analytical thinking, with the ability to troubleshoot complex issues and optimize system performance. • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.


• Competitive salary: The industry standard salary for Lead ML Operations Engineers typically ranges from $150,000 to $250,000 per year, depending on experience and qualifications. • Comprehensive health, dental, and vision insurance plans. • Flexible work hours and remote work options. • Generous vacation and paid time off. • Professional development opportunities, including access to training programs, conferences, and workshops. • State-of-the-art technology environment with access to cutting-edge tools and resources. • Vibrant and inclusive company culture with opportunities for growth and advancement. • Exciting projects with real-world impact at the forefront of MLOps innovation.

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