Overview
A MLOps Engineer is a professional who has several years of experience in MLOps and is expected to lead or independently manage large-scale projects related to ML model deployment, monitoring, and optimization. They also have a deeper understanding of systems architecture and can mentor junior team members.
Responsibilities
- Lead and design the architecture for deploying and maintaining ML models in production.
- Implement robust, scalable, and secure ML pipelines and workflows.
- Work closely with development teams to deploy and integrate models.
- Ensure the system is optimized for high availability, reliability, and performance.
- Automate end-to-end model deployment processes and CI/CD pipelines.
- Implement monitoring and alerting for production models, ensuring uptime and performance.
- Conduct performance tuning and model updates based on real-time data and feedback.
- Manage infrastructure on cloud services and container orchestration systems like Kubernetes.
- Mentor junior MLOps engineers and interns, providing technical guidance and reviews.
Required Skills and Qualifications
- Proficiency in English, with exceptional listening, speaking, reading skills
- Deep knowledge of MLOps lifecycle and operational challenges.
- Strong proficiency in programming (Python, Shell scripting).
- Expertise in cloud platforms, containerization, and orchestration (Kubernetes).
- Experience with cloud platforms like Azure, AWS, or Google Cloud, especially their AI/ML offerings.
- Experience in setting up and managing ML pipelines using tools like Kubeflow, MLflow, or Airflow.
- Strong troubleshooting and performance optimization skills.
- Experience with versioning, testing, and deployment strategies in ML workflows.
- Familiarity with security and compliance best practices in ML deployment.
- Excellent problem-solving, mentoring, and communication skills.
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