AI Cloud Infra Architect — Multi-Cloud & Production Ready

NCS Pte Ltd

Singapore

On-site

SGD 140,000 - 240,000

Full time

8 days ago

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Job summary

NCS is seeking a Cloud Engineer/Architect for AI Infrastructure within NCS AI Central's Forward Deployed Engineering model. You will own cloud infrastructure end-to-end, from architecture through hands-on provisioning and operations, across multi-cloud environments including AWS, Azure, and GCP, with Singapore government environments where applicable.

The role blends architecture leadership with hands-on delivery, covering DevOps, security, observability, and Gen AI infrastructure support to

Qualifications

3–5 years of relevant experience. Provisions and operates cloud infrastructure for individual engagements — hands-on deployment, troubleshooting, and monitoring — under guidance from a Senior Cloud Architect.
6–7+ years of relevant IT/cloud experience, including at least 2 years hands-on across multiple cloud environments (AWS, Azure, GCP) or deep hands-on within Singapore Government GCC/HCC environments. Owns cloud architecture end-to-end across multiple engagements.

Responsibilities

Architect and provision cloud infrastructure end-to-end — from design through hands-on deployment — across AWS, Azure, or GCP depending on the client's environment (e.g. AWS ECS Fargate/Lambda/RDS/OpenSearch Serverless; Azure Container Apps/Functions/Azure Database/AI Search; GCP Cloud Run/Cloud Functions/Cloud SQL/Vertex AI Search).
Deploy backend services, APIs, and AI pipelines, ensuring connectivity, security groups/NSGs, IAM roles, and networking (VPC/VNet, load balancing, DNS) are configured correctly.
Architect microservices and API architectures, serverless and container-based systems, and event-driven/streaming pipelines.
Apply working knowledge across at least two major cloud providers (AWS, Azure, or GCP) to support engagements regardless of client cloud posture, including Singapore Government GCC/HCC landing zones.
Deliver secure foundations / landing zones and cloud migrations across cloud platforms.
Apply strong knowledge of VPC/VNet design, NAT/Transit, subnets, IAM, KMS/Key Vault, autoscaling, resiliency, disaster recovery, and cost optimization strategies.
Apply cloud security principles, common threats, and mitigation techniques.
Set up and maintain CI/CD pipelines, from lightweight pipelines for rapid POC deployment through to production‑grade deployment automation, using Terraform / CDK / CloudFormation, Docker, and GitOps.
Implement observability stacks (CloudWatch, X‑Ray, OpenTelemetry) and use basic scripting/automation (Python, Bash) for day‑to‑day operational and troubleshooting tasks.
Enable backend and AI Engineering teams to integrate foundation models via AWS Bedrock, Azure AI Foundry, or Google Vertex AI — including China-origin models (DeepSeek, Qwen, GLM) where self-hosted or exposed via compatible endpoints; ensure model endpoints, API keys, and integration pipelines are functional.
Apply solid understanding of generative AI inference workloads, embeddings, vector search, RAG patterns, agentic workflows, and LLMOps practices, plus API rate limiting and workload isolation.
Apply exposure to API management frameworks such as Apigee or WSO2, in addition to native cloud API gateways.
During FDE engagements: stand up lightweight, disposable cloud environments that let the team iterate quickly on POC/POV without operational overhead.
During system development & maintenance engagements: take ownership of steady-state production operations, scaling, and incident troubleshooting for live cloud infrastructure.
Document cloud setup, architecture decisions, and deployment steps for the team.
Troubleshoot cloud infrastructure quickly and independently, across both POC and production contexts.
Work closely with the Application Architect and Fullstack Developer to keep application and infrastructure design aligned.