MLOps Project Manager

Seattle. WA (HYBRID)

Skills and Qualifications

Programming languages: Python and R, preferred- might consider Java instead

Machine learning frameworks: (TensorFlow, PyTorch, etc.); strong understanding of data engineering principles.

MLOps Knowledge: Familiarity with tools and practices for model deployment and monitoring, such as Kubernetes, Jenkins, MLflow, and Docker.

Leadership: Proven experience in leading teams and managing projects, excellent communication skills, and the ability to mentor and develop team members.

Architecture and Infrastructure:

Oversee the architecture of machine learning systems, ensuring scalability, reliability, and performance.

Work with cloud providers (AWS, Azure, Google Cloud) and on-premise solutions to optimize resource utilization and cost-effectiveness.

Model Management:

Develop and manage workflows for model training, validation, deployment, and monitoring.

Implement MLOps practices to streamline the lifecycle of machine learning models.

Collaboration:

Collaborate with cross-functional teams, including product management, data analytics, and IT, to align machine learning initiatives with business goals.

Stay informed about industry trends and emerging technologies to leverage new advancements in the field.

Stakeholder Engagement:

Communicate effectively with stakeholders to understand their needs and translate them into technical requirements and solutions.

Prepare and present reports or dashboards on the performance of machine learning initiatives.

Compliance and Ethics:

Ensure that machine learning practices align with ethical guidelines, data privacy regulations, and organizational policies.