AI/ML Architect - Enterprise Kubernetes

About the role

The Singapore AI Center of Excellence (COE) is an engineering and R&D hub focused on Enterprise and Sovereign AI, helping organizations move AI workflows into production through reusable software architectures and contributions to upstream open-source projects. The AI/ML Architect is a customer-facing technical authority within Customer Engineering who designs production-ready, repeatable architectural blueprints and leads technical scoping with strategic customers and partners. Career growth depends on technical competence and architectural depth, with no expectation of team management.

Key responsibilities

• Design and document production-ready system architecture blueprints and reference topologies, including hardening, compute/network/storage optimization, security boundaries and multi-tier IT landscapes.
• Lead customer workshops, technical scoping and custom proofs-of-concept, and hold architectural walk-throughs to validate feasibility.
• Advise regional engineering teams and executives, and act as a liaison between APAC customer needs and global product roadmaps.
• Work with chip manufacturers, ISVs and system integrators on joint-architecture solutions.
• Take full ownership of complex enterprise opportunities and advise C-level stakeholders on platform maturity, multi-year adoption and compliance.

Required profile

• Bachelor's degree or higher in Computer Science, Computer Engineering or a related quantitative field.
• Proven track record of leading technical consulting, system design or pre-sales architecture engagements for enterprise customers.
• Exceptional verbal and written English, able to explain complex architectures to technical and non-technical audiences.

Required skills

• Enterprise Kubernetes or container platforms, including multi-tenant clusters, container networking and persistent storage.
• Python and scripting/automation tools such as Git and Ansible/Terraform.
• Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow or Jupyter on containerized platforms.
• Nice to have: MLOps (vLLM, KServe, Ray, MLflow, Kubeflow), GitOps (ArgoCD, Flux), observability (Prometheus, Grafana, OpenTelemetry), Go/Java/Rust, and domain experience in financial services, public sector/healthcare or telecom/manufacturing.