Machine Learning Ops Engineer

Salla is seeking an accomplished MLOps Engineer to become a member of our team. In the role of MLOps Engineer, you will be tasked with the design, implementation, and maintenance of our machine learning infrastructure.

You will collaborate closely with our machine learning engineers and data scientists to ensure the efficient and seamless operation of our machine learning models. The ideal candidate will possess a robust background in computer science, machine learning, software engineering, as well as expertise in the design and implementation of cloud systems. The candidate should also have experience with MLOps frameworks such as Kubeflow, MLFlow, DataRobot, and Airflow, in addition to familiarity with containerization technologies like Docker and Kubernetes.

• Design and implement cloud solutions for machine learning infrastructure, build MLOps on cloud (AWS, Azure, or GCP).
• Build CI/CD pipelines orchestration by Jenkins CI, GitHub Actions, Circle CI, Airflow or similar tools.
• Collaborate with machine learning engineers to deploy machine learning models, and document the processes.
• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
• Data science models testing, validation and tests automation.
• Build custom integrations between cloud-based systems using APIs.
• Build and maintain automated data pipelines.
• Support model development, with an emphasis on auditability, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept machine learning systems.
• Implement and maintain monitoring and logging systems.
• Develop automation tools to improve the efficiency of processes.
• Ensure our machine learning infrastructure is reliable, scalable, and secure.
• Troubleshoot and resolve infrastructure issues.