About the role
Job Title: MLOps Engineer
Job Description:
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.
Responsibilities:
* 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 DevOps 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. * Implement continuous integration/continuous deployment (CI/CD) practices for ML model development lifecycle management. * 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.
Requirements:
* 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, TensorFlow 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/GitHub for managing code repositories * Excellent problem-solving skills with the ability to analyze complex technical issues related to ML model deployments.
Location:
Remote/Dallas.
Originally posted on Himalayas