Machine Learning Ops Engineer, YouLend

• ML in Practice: Proven experience building and maintaining operational ML systems
• Python Proficiency: Strong Python coding skills and familiarity with relevant ML/data engineering libraries
• DevOps Mindset: Understanding of CI/CD principles, containerisation (Docker, Kubernetes), and version control (Git)
• Problem-Solving Approach: Ability to troubleshoot issues in complex ML pipelines and proactively address potential bottlenecks

Desirable

• As our ML Ops Engineer, you'll be the architect of robust processes to streamline and maintain our machine learning models in a dynamic production environment
• You'll bridge the gap between research and deployment, ensuring our platform continues to deliver transformative results for our development partners
• This role requires a blend of data engineering, software development, and an understanding of how to operationalize Machine Learning
• Production Pipelines: Design, implement, and optimise scalable ML pipelines. This includes model training, validation, deployment, and monitoring
• Dataflow for Continuous Learning: Engineer efficient data infrastructure for handling simulation results and production usage data
• Alerting & Performance Monitoring: Set up systems to track model performance in production, creating alerts to flag potential accuracy degradation related to new geometries explored by our partners
• Automation & Tooling: Build and select tools to automate as many aspects of the ML lifecycle as possible, streamlining operations and model updates
• Collaboration is Key: Work closely with ML research engineers to understand new model advancements and integrate them into the production environment. Partner with software engineers for seamless platform functionalities