MLOps Engineer

We are seeking a highly skilled and experienced MLOps Engineer to join our client. As an MLOps Engineer, you will play a crucial role in developing and implementing machine learning operations processes and infrastructure to support our data science initiatives.

• Develop and maintain end-to-end machine learning operations (MLOps) pipelines for deploying, monitoring, and scaling machine learning models.
• Collaborate with data scientists, software engineers, and Dev Ops teams to ensure seamless integration of ML models into production systems.
• Design and implement automated testing frameworks for ML models to ensure accuracy, reliability, and performance.
• Optimize model deployment processes by leveraging containerization technologies such as Docker or Kubernetes.
• Monitor deployed ML models in production environments to identify performance issues or anomalies.
• Work closely with cross-functional teams to troubleshoot issues related to model performance or data quality in production systems.
• Stay up-to-date with the latest advancements in MLOps toolkits, frameworks, best practices, and industry trends.

• Bachelors degree in computer science or a related field; advanced degree preferred.
• Minimum 5 years of experience working as an MLOps Engineer or similar role within a data-driven organization.
• Experience with Kubernetes and Kubeflow is mandatory.
• Strong understanding of machine learning concepts and algorithms.
• Proficiency in Python developing ML pipelines/scripts.
• Experience with popular MLOps toolkits such as Kubeflow Pipelines, Tensor Flow Extended (TFX), MLflow, etc., is essential.
• Solid knowledge of containerization technologies like Docker and Kubernetes for deploying ML models at scale.
• Familiarity with cloud platforms like AWS/Azure/GCP for building scalable infrastructure solutions is highly desirable.
• Experience with version control systems like Git/Git Hub for managing code repositories.
• Excellent problem-solving skills with the ability to analyze complex technical issues related to ML model deployments.