Deploy Healthcare AI on Huawei Cloud
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About this role
Cloud Computing, Machine Learning (ML), Documentation, Kubernetes, DevOps, Microservices · I already have a healthcare-focused AI solution ready for production; what I need now is an engineer who truly knows Huawei Cloud inside out to turn it into a robust, scalable service for a hospital environment.
Scope The application will automate key hospital workflows, with two immediate priorities: real-time patient monitoring and intelligent staff scheduling. Low-latency inference, strong uptime, and strict data-security controls comparable to HIPAA are non-negotiable. You may propose the best mix of ModelArts, ECS, CCE/Kubernetes, Cloud Eye, and any other Huawei Cloud services that keep costs sensible while preserving performance.
Key deliverables • End-to-end deployment pipeline (code → ModelArts training/inference → production) • Secure APIs or micro-services exposing patient-monitoring and staff-scheduling endpoints • Auto-scaling and monitoring dashboards configured in Cloud Eye • Deployment documentation and hand-off session so my in-house team can maintain and iterate
Acceptance criteria High Throughput Faster Response End to end testing Scalable