Careers

MLops Engineer

Posted: 21/03/2021

Lead person opportunity to join a multi-award-winning tech company in the automotive sector scaling up to service a rapidly expanding global customer base. This role is to work designing and building the infrastructure to process millions of vehicle images through cloud AI & ML systems. There are requirements to manage large datasets of images for ML training infrastructure, and serve models with high performance to handle the throughput required to support our increasing system deployments. This role is to manage the tools and infrastructure to support the ML team and meet the serving needs of the production systems. In the future, there is scope to expand to CI/CD systems for the ML pipeline and models, and investigate issues around edge deployments.

Duties/Expectations

 

  • Lead and implement the data management and ML training infrastructure
  • Improve resilience with data control systems and cloud architecture
  • Develop cloud training infrastructure to support ML team deployments
  • Design and implement model serving infrastructure to extend current systems
  • Support multiple model types, versioning and deployments, as well as monitoring metrics.
  • Recommend different ways to constantly improve data reliability and quality

SKILLS/QUALIFICATIONS

  • Degree in Computer Science, Software/Computer Engineering, Applied Mathematics or related discipline
  • Deep knowledge of infrastructure as code, and ML pipelines
  • Full understanding of AWS, Python and Linux systems administration
  • Knowledge of containers with MLflow and Kubeflow an advantage
  • Strong analytical mindset and problem-solving skills
  • Excellent communication and organisational skills

EXPERIENCE

  • Strong experience in cloud architecture, DevOps and or ML system management
  • Demonstratable experience with AWS, Python, Linux system administration and containers
  • Image processing experience
  • Scale-up experience an advantage

SALARY & BENEFITS

  • Competitive salary and benefits
  • Remote working considered
Essential Experience Desirable Experience (Non-Essential)
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