ML Ops Engineer

Job Title: ML Ops Engineer Location: Remote Experience Required: 3 - 5 Years Job Overview: Lead the strategic implementation and management of Machine Learning Operations (ML Ops) to streamline the deployment and maintenance of machine learning models, ensuring their seamless integration into production environments. Collaborate closely with data scientists and IT teams to bridge the gap between development and operations, enhancing the efficiency and reliability of ML workflows. Key Responsibilities:

• Model Deployment and Automation:
• Develop and maintain automated pipelines for model deployment, monitoring, and scaling, ensuring efficient and robust integration into production systems.
• Implement best practices for model version control, model tracking, and performance optimization to ensure reliability and reproducibility of ML models.
• ML Ops Integration and Collaboration:
• Collaborate with DevOps teams to integrate ML Ops processes into existing CI/CD pipelines, facilitating continuous deployment and rapid iteration of ML models.
• Work closely with data scientists to streamline the model development lifecycle, from experimentation to production deployment.
• Monitoring and Performance Management:
• Establish monitoring frameworks to track model performance and detect anomalies, ensuring optimal functioning of ML models in production.
• Develop and implement strategies for model retraining and updates based on performance metrics and feedback.
• Documentation and Compliance:
• Create and maintain comprehensive documentation for ML Ops processes, including deployment pipelines, monitoring tools, and model performance metrics.
• Ensure adherence to compliance standards and data security protocols throughout the ML lifecycle.

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Proven experience in ML Ops or DevOps roles, with a strong understanding of machine learning lifecycle management.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.

• Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying and managing ML models.
• Proficiency with DevOps tools such as Kubernetes, Docker, and Jenkins for CI/CD pipeline integration.
• Strong understanding of infrastructure-as-code (IaC) tools and principles to automate infrastructure setup and management.