ML Ops Site Reliability Engineer

Job Description

We are seeking a highly skilled and motivated MLOps Site Reliability Engineer (SRE) to join our team. In this role, you will be responsible for ensuring the reliability, scalability, and performance of our machine learning infrastructure. You will work closely with data scientists, machine learning engineers, and software developers to build and maintain robust and efficient systems that support our machine learning workflows. This position offers an exciting opportunity to work on cutting-edge technologies and make a significant impact on our organization's success. Design, implement, and maintain scalable and reliable machine learning infrastructure. Collaborate with data scientists and machine learning engineers to deploy and manage machine learning models in production. Develop and maintain CI/CD pipelines for machine learning workflows. Monitor and optimize the performance of machine learning systems and infrastructure. Implement and manage automated testing and validation processes for machine learning models. Ensure the security and compliance of machine learning systems and data. Troubleshoot and resolve issues related to machine learning infrastructure and workflows. Document processes, procedures, and best practices for machine learning operations. Stay up-to-date with the latest developments in MLOps and related technologies. Qualifications Required: Bachelor's degree in Computer Science, Engineering, or a related field. Proven experience as a Site Reliability Engineer (SRE) or in a similar role. Strong knowledge of machine learning concepts and workflows. Proficiency in programming languages such as Python, Java, or Go. Experience with cloud platforms such as AWS, Azure, or Google Cloud. Familiarity with containerization technologies like Docker and Kubernetes. Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI. Strong problem-solving skills and the ability to troubleshoot complex issues. Excellent communication and collaboration skills. Preferred Master's degree in Computer Science, Engineering, or a related field. Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. Knowledge of data engineering and data pipeline tools such as Apache Spark, Apache Kafka, or Airflow. Experience with monitoring and logging tools such as Prometheus, Grafana, or ELK stack. Familiarity with infrastructure as code (IaC) tools like Terraform or Ansible. Experience with automated testing frameworks for machine learning models. Knowledge of security best practices for machine learning systems and data. Show more Show less