Urgent Need : Senior MLOps_SRE_DevOPS Engineer : Remote : Contract

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Overview

The Senior MLOps_SRE_DevOps Engineer position at Dice is a crucial role for managing, deploying, and scaling machine learning models in production environments. The successful candidate will work closely with data scientists and software engineering teams to streamline the integration of machine learning workflows, enhance the operational life cycle, and ensure system reliability and scalability. This role requires a deep understanding of both machine learning processes and DevOps practices to design robust infrastructure to support complex applications.

Job Responsibilities

• Design, build, and maintain scalable and reliable MLOps platforms.
• Collaborate with data scientists to operationalize machine learning models and ensure smooth deployment into production environments.
• Implement CI/CD practices to automate the deployment pipeline.
• Monitor system performance and troubleshoot issues related to uptime and reliability.
• Ensure best practices for security and compliance across all infrastructure and data management activities.
• Document processes and provide training to team members on MLOps tools and practices.
• Stay updated on industry trends and technologies to continually enhance the MLOps infrastructure.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Minimum of 5 years of experience in DevOps or Site Reliability Engineering, with a focus on machine learning operations.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with MLOps frameworks and tools like MLflow, Kubeflow, or TFX.
• Strong knowledge of cloud platforms such as AWS, Google Cloud, or Azure.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Expertise in CI/CD methodologies and tools such as Jenkins, CircleCI, or GitLab CI.

Technologies & Tools

This position utilizes a variety of technologies and tools essential to the MLOps landscape, including but not limited to Docker, Kubernetes, TensorFlow, PyTorch, Jenkins, and various cloud services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). Familiarity with monitoring tools like Prometheus or Grafana will also be advantageous in ensuring system performance and reliability.

Ideal Candidates

The ideal candidate is a self-motivated and detail-oriented professional with a strong analytical mindset. They possess excellent problem-solving skills and can work effectively under pressure. Strong communication skills are essential for collaborating with cross-functional teams, and adaptability to new technologies and workflows is vital in this rapidly evolving field. A passion for machine learning and operational excellence will distinguish the most suitable candidates for this role.