MLOps Architect

Manadate Skills: Kubeflow, MLflow, 2-3 complex MLOps architectures in the cloud platform, End to End architecture and deployed Computer Vision and Predictive analytics complex use cases running in production

Over 12 years of experience, including more than 5 years in the MLOps Engineer role and 3 years as an MLOps Architect. Independent handling of at least three production deployments is required.

• Design and implement cloud solutions and build MLOps on cloud platforms (AWS, Azure, or GCP).
• Build CICDCL (Continuous Machine Learning) and CT (Continuous Training) pipeline orchestration using tools like MLflow, Kubeflow, Kubernetes, GitLab CI, GitHub Actions, CircleCI, Airflow, or similar.
• Communicate with teams of data scientists, data engineers, and application architects, and document processes.
• Assist in deploying machine learning models into production environments.
• Contribute to the monitoring and maintenance of model performance and infrastructure health.
• Participate in the development and maintenance of automated MLOps pipelines.
• Collaborate with cross-functional teams to integrate machine learning models into production systems.
• Maintain documentation of MLOps processes and procedures.
• Work closely with technology team members in the development and implementation of AI solutions, products, and platforms.
• Knowledgeable in agile practices.