Sr. MLOps/DevOps Engineer

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

Job Overview

As a Senior MLOps/DevOps Engineer at Q Consulting, you will play a pivotal role in managing and optimizing the machine learning lifecycle, ensuring seamless deployment, monitoring, and scaling of ML models in production. This position requires collaboration with data scientists, software engineers, and IT teams to enhance system performance and reliability. You will be responsible for implementing best practices for CI/CD, automation, and infrastructure as code, facilitating a streamlined development workflow that aligns with the organization's goals.

Job Responsibilities

• Design, build, and maintain scalable ML deployment pipelines.
• Implement CI/CD processes for automated testing and deployment of ML models.
• Monitor and optimize the performance of ML models in production environments.
• Collaborate with cross-functional teams to ensure alignment on ML workflow and infrastructure requirements.
• Develop and manage infrastructure as code using tools such as Terraform or CloudFormation.
• Troubleshoot and resolve issues related to deployment, performance, and reliability of ML solutions.
• Stay updated on industry trends and best practices in MLOps and DevOps methodologies.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; Master's degree preferred.
• Minimum of 5 years of experience in DevOps, MLOps, or related fields.
• Proficient with cloud platforms (AWS, GCP, or Azure) and their respective services for building ML infrastructure.
• Strong programming skills in Python, Java, or similar languages.
• Experience with container orchestration tools such as Kubernetes and Docker.
• Familiarity with ML frameworks like TensorFlow, PyTorch, or similar.
• Solid understanding of CI/CD tools like Jenkins, GitLab CI, or CircleCI.
• Excellent problem-solving and analytical skills with a focus on customer-centric solutions.

Technologies & Tools

In this position, you will be utilizing a suite of technologies and tools to enhance the deployment and management of ML models. Key tools include cloud platforms like AWS, GCP, or Azure for computing and storage solutions; CI/CD tools such as Jenkins or GitLab CI for automation; containerization technologies like Docker and Kubernetes for orchestration; and version control systems like Git for code management. Familiarity with ML frameworks such as TensorFlow or PyTorch is also essential for your role in integrating model deployment into production workflows.

Ideal Candidates

The ideal candidates for the Senior MLOps/DevOps Engineer position will possess strong technical acumen and a proactive approach to problem-solving. They will demonstrate exceptional collaboration capabilities, working effectively across multidisciplinary teams. A passion for continual learning and adaptability to evolving technologies is essential, as well as a commitment to optimizing performance and efficiency in all aspects of ML operations. Candidates should embody a customer-focused mindset, ensuring that all solutions meet client needs and business objectives effectively.