#EG Cloud Engineer / Architect – AI Infrastructure

NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group. This role sits within NCS AI Central's (AIC) Forward Deployed Engineering (FDE) model — the combined capability that takes AI solutions from proof-of-concept through to hardened production systems. As Cloud Engineer/Architect, you own cloud infrastructure end-to-end — from architecture and design through hands-on provisioning and operations — across both fast-moving FDE engagements (POC/POV, pilot deployments) and steady-state system development and maintenance work. What will you do: 1. Cloud Architecture & Provisioning • 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), including Singapore Government GCC/HCC environments where applicable. • 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. 2. Multi-Cloud & Secure Delivery • 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. 3. DevOps & Platform Engineering • 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. 4. Gen AI Infrastructure Support • 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. 5. FDE & Development/Maintenance Coverage • 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. 6. Collaboration • 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. We are hiring at two levels for this role. All responsibilities above apply to both; the distinction is in scope of ownership, years of experience, and seniority of judgement expected. Cloud Engineer – AI Infrastructure • 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. • Executes against architecture decisions set by others; not yet expected to independently architect multi-service, multi-cloud environments. Senior Cloud Architect – AI Infrastructure • 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. • Sets infrastructure standards and security guardrails, leads cloud migrations and landing zone design, and mentors Cloud Engineers. Qualifications The ideal candidate should possess: • Strong hands-on experience with cloud container/serverless/database/search services on at least one major cloud provider (e.g. AWS: ECS Fargate, Bedrock, RDS Postgres, OpenSearch Serverless; Azure or GCP equivalents). • Proven ability to architect microservices, API, serverless, container-based, and event-driven/streaming systems. • Strong knowledge of VPC/VNet design, NAT/Transit, subnets, IAM, KMS/Key Vault, autoscaling, resiliency, disaster recovery, and cost optimization. • Cloud security principles, common threats, and mitigation techniques. • Experience with Terraform/CDK/CloudFormation, Docker, CI/CD, GitOps, and observability stacks. Tech Stack (Illustrative) • Platform: AWS (ECS Fargate, Lambda, RDS Postgres, OpenSearch Serverless, API Gateway); Azure (Container Apps, Functions, Azure AI Search); GCP (Cloud Run, Vertex AI Search) — including GCC/HCC • IaC & DevOps: Terrafo…