Remote MLOps DevOps Engineer | WFH

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Overview

The Remote MLOps DevOps Engineer at Get It - Professional Services will play a pivotal role in bridging the gap between machine learning and operational deployment. This position involves developing and maintaining infrastructure, automating workflows, and implementing best practices to enhance the efficiency and reliability of ML models. The ideal candidate will collaborate closely with data scientists and software engineers to streamline the ML development lifecycle, ensuring that models are production-ready and can be effectively monitored and maintained in a cloud environment.

Job Responsibilities

• Design, implement, and maintain MLOps workflows to ensure seamless integration of machine learning models into production.
• Automate data ingestion, model training, and deployment processes using modern CI/CD practices.
• Monitor and troubleshoot production ML systems to ensure optimal performance and availability.
• Collaborate with data scientists to refine models towards operational readiness, including data handling and model evaluation.
• Manage cloud infrastructure and services to support machine learning applications, ensuring scalability and security.
• Implement monitoring and logging systems for data pipelines and model performance analytics.
• Document processes, dependencies, and best practices to ensure knowledge transfer within the team.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Proven experience in MLOps, DevOps, or related roles with at least 3 years of professional experience.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Strong understanding of CI/CD pipelines, GIT workflows, and automated deployment practices.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model deployment tools (e.g., MLflow, Kubeflow).
• Ability to analyze and optimize complex systems and large datasets.
• Excellent problem-solving skills and a proactive approach to challenges.

Technologies & Tools

The Remote MLOps DevOps Engineer will work with a variety of technologies and tools including cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). Familiarity with Kubernetes for orchestration and Docker for containerization is essential. The position also requires leveraging tools like Terraform for infrastructure as code, CI/CD platforms such as Jenkins or GitLab CI, and monitoring tools including Prometheus and Grafana. Additional experience with machine learning frameworks like TensorFlow or PyTorch, and version control systems such as Git are highly desirable.

Ideal Candidates

Ideal candidates for this position possess a strong blend of technical expertise and interpersonal skills. They must demonstrate a continuous learning mindset, displaying enthusiasm for emerging technologies and methodologies in the MLOps space. Additionally, successful candidates will exhibit effective communication abilities, enabling them to work collaboratively across teams. They should be adaptable, able to thrive in a fast-paced environment, and possess a strong problem-solving acumen that drives them to seek innovative solutions. A passion for harnessing the power of machine learning to drive business outcomes will set standout candidates apart.