Purpose of Role:

• Frontline On-Call Ownership: Serve as the primary responder for the Applied Machine Learning Engine, taking ownership of system availability, health monitoring, and immediate incident response to ensure high reliability.
• Incident Lifecycle Management: Manage the end-to-end feedback loop for incidents, including rapid triage, effective resolution, and the facilitation of post-incident reviews to ensure closure and prevent recurrence.
• SOP Execution & Optimization: Execute upgrades and deployments strictly adhering to Standard Operating Procedures (SOPs), while actively leveraging Machine Learning and Infrastructure expertise to refine, automate, and improve these processes for greater efficiency.

Responsibilities:

• Analyse all kinds of user needs related to machine learning systems provided by AML department , through oncall shifting or any other mechanisms, then propose customer oriented solutions .
• Work with other software engineers to implement and deploy customer-oriented machine learning framework related solutions which are proposed by oneself or not .
• Update software, enhances existing software capabilities, and develops or deploy software testing 、deployment 、capacity management and validation procedures.
• Work with computer hardware engineers to integrate hardware and software systems and trouble-shooting specifications and performance requirements.

Minimum requirements:

• Bachelor’s degree in Computer Science or equivalent with 3+ years of relevant experience
• Proven experience in analyzing and troubleshooting distributed systems.
• Prior experience designing or maintaining large-scale systems.
• Scripting skills in at least one major language (Python, Go, or Shell/Bash) to automate repetitive operational tasks.

Nice to have:

• Experience defining and managing Service Level Indicators (SLIs), Service Level Objectives (SLOs), error budgets, and practicing Chaos Engineering.
• Experience operating MLOps platforms and toolkits such as Kubeflow, MLflow, Feast, or Ray.
• Deep understanding of Linux operating system internals or container technologies (Docker/Containerd) and orchestration platforms (Kubernetes) in a production environment.
• Basic understanding of Machine Learning concepts and familiarity with frameworks like TensorFlow Serving, TorchServe, or Triton Inference Server