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
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