Staff Engineer MLOps & ML Engineering

As a machine learning operations leader, together with Pattern's Data Science and Engineering teams, you will lead a team that creates and maintains impactful solutions for our brands across the world. From traditional machine learning to large language models, you will work and lead throughout the model lifecycle.

Responsibilities:

• Leadership: MLOps is a team sport, and we require a leader who can elevate everyone in the MLOps organization. While technical skills and vision are required, your leadership skills will take AI and machine learning from theoretical to operational, delivering tangible value to both customers and internal teams.
• Pipeline Management: Architect, implement, and maintain scalable ML pipelines, with seamless integration from data ingestion to production deployment.
• Model Monitoring:  Lead the operationalization of machine learning models, ensuring hundreds of models are continuously monitored, retrained, and optimized in real-time environments
• Deployment:  Deploy machine learning solutions in the cloud, securely and cost effectively.
• Reporting: Effectively communicate actionable insights across teams using both automatic (e.g., alerts) and non-automatic methods.

The type of game changing candidate we are looking for:

• Seasoned: Demonstrated experience successfully leading teams both formally and informally.
• Transparent: Willingness to identify and admit errors and seek out opportunities to continually improve both in their own work and across the team.
• Communication: MLOps is a central node in a complex system. Clear, actionable, and concise communication, both written and verbal is a must.
• Coaching and Team Advancement: An MLOps leader is continually developing team members and fostering a constant flow of communication and improvement across team members.
• Master's/PhD degree or a strong demonstration of technical expertise in Computer Science, Machine Learning, Data Science, or a related field
• Multiple years of direct extensive experience with AWS
• Multiple years of experience with MLOps monitoring and testing tools
• Ability to prioritize projects effectively once clear vision and goals are identified
• Excited to empower DS with tools, practices, and training that simplify MLOps enough for Data Science to increasingly practice MLOps on their own and own products in production.