MLOps Engineer

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, and reading skills.
• Deep knowledge of the 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.