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Machine Learning Operations Strategist

1 month ago


San Francisco, California, United States Unreal Gigs Full time
Job Summary

We are seeking an experienced Machine Learning Operations Strategist to lead our MLOps efforts and drive the design, implementation, and optimization of infrastructure and processes for deploying, monitoring, and managing machine learning models at scale.

This role requires a seasoned engineer with a strong understanding of machine learning concepts and techniques, as well as leadership experience in driving innovation and execution. The ideal candidate will have a passion for machine learning, a track record of designing and implementing MLOps solutions, and excellent communication and collaboration skills.

Key Responsibilities
  • 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.
Requirements
  • 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.
  • Programming languages: Proficiency in Python, Java, or Scala, and experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Containerization technologies: Experience with Docker and orchestration tools such as Kubernetes.
  • Machine learning frameworks: Familiarity with TensorFlow, PyTorch, scikit-learn, or MLflow.
  • CI/CD pipelines: Experience with Jenkins, GitLab, or CircleCI.
  • Problem-solving skills: Strong analytical thinking, with the ability to troubleshoot complex issues and optimize system performance.
  • Communication skills: Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.
Salary and Benefits

The estimated salary range for this position is $170,000 - $220,000 per year, depending on experience and qualifications. Additionally, we offer comprehensive health, dental, and vision insurance plans, flexible work hours, remote work options, generous vacation and paid time off, professional development opportunities, and a state-of-the-art technology environment with access to cutting-edge tools and resources.