ncs3
#EG Cloud Engineer / Architect – AI Infrastructure
This role involves architecting and managing cloud infrastructure for AI solutions, ranging from proof-of-concept environments to production-grade systems. The engineer will work across AWS, Azure, and GCP to deploy secure, scalable infrastructure and integrate generative AI models.
- Compensation
- Not disclosed
- Location
- Singapore, sg
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Devops
- Posted
Skills
Must have
- 3-7+ years of relevant IT/cloud experience
- Hands-on experience with cloud container, serverless, database, and search services on at least one major cloud provider
- Ability to architect microservices, API, serverless, container-based, and event-driven/streaming systems
- Strong knowledge of VPC/VNet design, IAM, KMS/Key Vault, autoscaling, and disaster recovery
- Experience with Terraform, CDK, CloudFormation, Docker, CI/CD, GitOps, and observability stacks
- Understanding of cloud security principles and mitigation techniques
Full description
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.