Apple's cameras deploy imaging complexity at the frontier of traditional camera engineering methods. Data volumes are growing to meet this need across camera simulations, performance calibrations, measurement results, and their correlations. The team's task is to build a comprehensive aggregate data layer that enables efficient and flexible executive reporting, highly customized data applications, and powerful ML inference and analysis.
Requirements
- Experience managing cloud-native applications leveraging AWS including EKS, Kubernetes and S3
- Observability and telemetry tools such as CloudWatch, Splunk, Prometheus, OpenTelemetry
- Experience with tools such as Helm, Flux, ArgoCD
- Experience with large-scale observability visualization systems with knowledge of popular visualization tools like Grafana, Data Dog, and ELK stack
- Ability to build CI pipelines and automation tools using tools such as Jenkins, GitHub Actions, CircleCI, etc.
- Experience with Infrastructure-as-Code (IaC) tools such as Terraform, OpenTofu, or Pulumi
- Working knowledge with Kubernetes networking
Responsibilities
- manage the deployment of scalable cloud-native services
- build observability tools
- implement infrastructure as code automation
- create AI agents in a modern data layer
- lead the design and development of observability solutions for Camera Data Engineering products and infrastructure
- use open source and internal tools to automate and manage the deployment of our application stack across public and on-prem cloud providers
- implement best practices for application observability and alerting
Other
- BS or higher in Computer Science in Data Engineering, Data Science, or related fields.
- providing technical guidance
- sharing observability know-how
- leveraging AI pipelines
- mentoring the team
