AI/ML Architect

About The Singapore AI Center Of Excellence (COE)

The Singapore AI Center of Excellence (COE) is a dedicated engineering and R&D hub focused on Enterprise and Sovereign AI. Our mission is to help organizations move AI workflows cleanly into production through two main paths: creating reusable software architectures that solve common industry problems, and contributing code directly to upstream open-source projects to fix enterprise gaps in system deployment, runtime tuning, and platform management.

Basing our engineering team in Singapore creates a close feedback loop between customers, partners, and core product teams. This direct connection keeps our development roadmaps relevant, speeds up solution delivery, and strengthens our ability to co-innovate across the region.

Role Overview

The AI/ML Architect is a highly consultative, customer-facing technical authority role at the intersection of Enterprise AI and client-facing infrastructure design. As part of our Customer Engineering function,, you will design production-ready, repeatable architectural blueprints and lead technical scoping alongside strategic customers and technology partners. Your mission is to eliminate technical and organizational friction to establish modern, enterprise-grade Kubernetes container systems as the preferred platform for high-scale application and AI workloads.

In this team, career growth and seniority are defined purely by your technical competence, architectural depth, and ability to deliver end-to-end solutions autonomously in highly ambiguous environments while mentoring technically AI/ML engineering colleagues. There is no expectation of team management or administrative coordination; your progression is driven entirely by engineering design impact, advisory leadership and your ability to coach and develop junior team members.

What You Will Do

Enterprise-Minded Blueprinting: Design and document comprehensive, production-ready system architecture blueprints and reference topologies. These blueprints must naturally take into account critical enterprise requirements—including systems-level hardening, compute/network/storage optimization, security boundaries, and multi-tier IT landscapes.
Client Engineering Workshops & POCs: Lead the technical scoping, discovery, and execution of deep-dive customer workshops. Design custom Proofs-of-Concept (POCs) and architectural walk-throughs to validate technical feasibility before down-stream delivery teams begin implementation.
Strategic Advisory & Client Advocacy: Serve as a trusted technical advisor to regional engineering teams and corporate executives, translating complex AI/ML requirements and technology strategies into actionable, high-value business outcomes.
Ecosystem & Partner Integration: Collaborate with external technology ecosystems (such as chip manufacturers, independent software vendors, and regional system integrators) to design and validate joint-architecture solutions.
Feedback Loop Optimization: Act as a key technical liaison between APAC market realities and product management teams, channeling regional customer feedback and infrastructure requirements into global product roadmaps.
Autonomous High-Stakes Delivery (Senior Level): Take full technical ownership of complex, strategic enterprise opportunities. Advise senior IT and business executives (including C-level stakeholders) on platform maturity, multi-year adoption strategies, and compliance frameworks.

What You Will Bring

To be considered for this role, all candidates must meet the following baseline requirements:

Academic & Professional Experience

Education: Bachelor’s degree or higher in Computer Science, Computer Engineering, or a related quantitative field.
Customer Delivery Experience: Proven track record of leading successful technical consulting, system design, or pre-sales architecture engagements for enterprise customers.
Communication: Exceptional verbal and written communication skills in English, with a demonstrated ability to distill and present complex technical architectures to both technical and non-technical stakeholder groups.

Core Technical Stack

Cloud-Native Mastery: Demonstrated expertise in architecting and managing enterprise-grade Kubernetes or container platform environments, specializing in multi-tenant cluster structures, container networking, and persistent storage strategies.
Programming & Scripting: Strong application architecture and development foundations, with hands-on proficiency in Python and standard scripting/automation tools (such as Git and Ansible/Terraform).
AI/ML Platforms & Frameworks: Practical experience running, configuring, and trouble-shooting AI/ML frameworks (such as PyTorch, TensorFlow, or Jupyter environments) on containerized platforms.

Target Knowledge Domains & Growth Areas

Skills

We are building a multi-disciplinary engineering and architecture squad. Candidates are expected to bring experience in some of the following domains, and will have the opportunity to continuously develop their skills across all of them as they grow in seniority:

Advanced MLOps & Lifecycle Management: Hands-on architecture experience with model serving frameworks (such as vLLM, KServe, or Ray), workflow orchestration (such as MLflow or Kubeflow), model registries, and ML dataset preparation.
Cloud-Native Observability & GitOps: Proficiency in declarative GitOps deployment workflows (such as ArgoCD or Flux) and container observability/telemetry stacks (including Prometheus, Grafana, and OpenTelemetry).
Infrastructure Modernization & GPU Clustering: Strong familiarity with virtualization abstractions, bare-metal server orchestration, and network topologies optimized for high-performance computing (including GPU slicing and high-throughput clustering).
Regulated Deployments & Secure Design: Designing isolated, air-gapped container networks, secure identity models, and localized inference environments to meet strict data privacy, risk management