AI Services Delivery Lead - On-Prem & Private Cloud

DSTA

Singapore

On-site

SGD 120,000 - 180,000

Full time

14 days+

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Job summary

DSTA is seeking an AI Service Delivery Manager to own a portfolio of live AI deployments running in on-premise and private cloud environments. You will ensure trusted, available and effective AI systems, taking accountability for service health, adoption, and escalation when needed.

Collaborate with AI product teams, infrastructure and engineering to bring new environments into service, coordinate upgrades, and drive post-incident reviews.

Qualifications

  • Hands-on experience supporting software deployed on infrastructure outside your direct control.
  • Working knowledge of Linux, containers, networking fundamentals, and at least one private cloud or virtualization platform.
  • Familiarity with Kubernetes and MLSecOps is advantageous.

Responsibilities

  • Own end-to-end service delivery for on-premise and private cloud AI deployments, meeting service levels for availability, latency, throughput and support.
  • Coordinate upgrades, patches, and configuration changes across environments and manage release processes.
  • Act as incident manager for major incidents, drive RCA, publish post-incident reviews with corrective actions.
  • Monitor production performance, identify degradation early, ensure auditability and traceability for changes.
  • Collaborate with AI products, infrastructure and engineering teams to stabilise new environments and transfer to service.
  • Drive continual service improvement and maintain runbooks for restricted environments.

Education

Degree in Computer Science, Computer Engineering and AI/ML

Tools

Prometheus
Grafana
OpenTelemetry

Job description

DSTA is seeking an AI Service Delivery Manager to own a portfolio of live AI deployments running in on-premise and private cloud environments. You will ensure trusted, available and effective AI systems, taking accountability for service health, adoption, and escalation when needed.

Collaborate with AI product teams, infrastructure and engineering to bring new environments into service, coordinate upgrades, and drive post-incident reviews.

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