DevOps/Machine Learning Operations Engineer

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Written by AI from the job title and company, not by Zelis, so some details may not match this job. The employer's own page has the real description.

Job Overview

The DevOps/Machine Learning Operations Engineer at Zelis will play a critical role in bridging the gap between software development and operations. This position is responsible for developing and maintaining the infrastructure necessary for machine learning model deployment, ensuring reliability, scalability, and continuous integration and delivery. The ideal candidate will implement best practices in DevOps methodologies while supporting the deployment and management of machine learning algorithms and data pipelines to support data-driven decision-making processes across the organization.

Job Responsibilities

• Design, implement, and manage CI/CD pipelines for machine learning model deployment.
• Collaborate with data scientists and stakeholders to understand and incorporate business requirements into operational processes.
• Monitor, troubleshoot, and optimize deployed models and infrastructure for performance and efficiency.
• Automate manual processes to minimize the risk of error and improve scalability.
• Refine and enhance existing MLOps frameworks and tools utilized for model deployment and management.
• Document operational practices, processes, and workflows to ensure transparency and reproducibility.
• Stay informed about emerging industry trends, tools, and methodologies in MLOps and DevOps.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field; Master’s degree preferred.
• 3+ years of experience in DevOps, MLOps, or software engineering roles.
• Proficiency in programming languages such as Python, Java, or Go.
• Experience with cloud platforms like AWS, Azure, or Google Cloud.
• Solid knowledge of containerization and orchestration technologies (Docker, Kubernetes).
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Strong understanding of version control systems (e.g., Git) and CI/CD tools (e.g., Jenkins, CircleCI).
• Excellent problem-solving skills and ability to work in a fast-paced environment.

Technologies & Tools

In this role, the DevOps/Machine Learning Operations Engineer will work extensively with a range of technologies and tools, including cloud computing environments (AWS, Azure), containerization platforms (Docker, Kubernetes), orchestration tools, and programming languages like Python and Java. Familiarity with machine learning libraries and frameworks such as TensorFlow and PyTorch will be essential, as well as experience with continuous integration and deployment (CI/CD) tools like Jenkins and GitLab.

Ideal Candidates

The ideal candidate for the DevOps/Machine Learning Operations Engineer position at Zelis will possess a combination of technical expertise, proactive problem-solving skills, and the ability to collaborate effectively across multidisciplinary teams. A strong focus on automation and continuous improvement, as well as excellent communication skills, will be essential for ensuring the successful deployment of machine learning solutions. The candidate should also exhibit a passion for learning and adapting to new technologies and industry practices, contributing to the innovation and growth of the organization.