MLOps Engineer

Inizio Partners Corp; Dallas (TX); On-site; USD 110,000 - 140,000; Full time

Job summary

A data science consultancy in Dallas is looking for a highly skilled MLOps Engineer to develop and implement machine learning operations. The ideal candidate should have at least 5 years of experience, strong skills in Kubernetes and Python, and be adept at developing end-to-end MLOps pipelines. This position plays a crucial role in supporting data science initiatives through effective infrastructure and operations.

Qualifications

• Minimum 5 years experience as an MLOps Engineer or similar role.
• Experience with Kubernetes and Kubeflow is mandatory.
• Proficiency in Python for developing ML pipelines.

Responsibilities

• Develop and maintain end-to-end MLOps pipelines.
• Collaborate with data scientists and software engineers.
• Design and implement automated testing frameworks for ML models.

Skills

MLOps pipelines development

Collaboration with cross-functional teams

Automated testing frameworks for ML

Model optimization using Docker/Kubernetes

Monitoring deployed ML models

Staying updated with MLOps trends

Education

Bachelor's degree in Computer Science

Tools

Kubernetes

Kubeflow

Python

Docker

AWS/Azure/GCP

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.
• 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.