MLOps Engineer

Inizio Partners Corp; United States; Remote; USD 140,000 - 170,000; Full time

Job summary

Inizio Partners Corp. is seeking an experienced MLOps Engineer to design and manage end-to-end ML operations, including deployment, monitoring, and scaling of models in production environments.

The role requires collaboration with data scientists and DevOps to ensure smooth integration into production systems, along with proactive testing and performance optimization. Remote-friendly with emphasis on scalable cloud infrastructure.

Qualifications

• Bachelor's degree in Computer Science or a related field required.
• Minimum 5 years of experience as an MLOps Engineer or similar role.
• Experience with Kubernetes and Kubeflow is mandatory.
• Strong understanding of machine learning concepts and algorithms.
• Proficiency in Python developing ML pipelines/scripts.
• Experience with Kubeflow Pipelines, TFX, MLflow, etc., is essential.
• Solid knowledge of Docker and Kubernetes for scaling deployments.
• Familiarity with AWS/Azure/GCP for scalable infrastructure is highly desirable.

Responsibilities

• Develop and maintain end-to-end MLOps pipelines for deploying, monitoring, and scaling ML models.
• Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into production systems.
• Design automated testing frameworks for ML models to ensure accuracy and reliability.
• Optimize deployment processes using Docker or Kubernetes.
• Implement CI/CD practices for the ML development lifecycle.
• Monitor deployed ML models in production to identify performance issues.
• Troubleshoot production issues related to model performance or data quality.
• Stay updated with the latest MLOps toolkits, frameworks, and best practices.

Skills

Kubernetes

Kubeflow

Python

ML concepts

CI/CD

Git/GitHub

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Advanced degree preferred

Tools

Kubeflow Pipelines

TensorFlow Extended (TFX)

MLflow

Docker

GitHub

Job description

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.