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.