AI Platform Architect

Morgan McKinley; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Morgan McKinley in Singapore seeks an experienced AI Platform Architect to lead the enterprise AI platform architecture, define standards and roadmaps, and drive secure, scalable AI adoption across the organization.

You will assess models and frameworks, establish guardrails, collaborate with cybersecurity and governance teams, mentor engineers, and present proposals to senior leadership. This role requires strong strategic and technical leadership in a complex AI environment.

Qualifications

• Degree in Computer Science, Computer Engineering, Software Engineering, Information Systems or a related technical discipline.
• At least 10 years of experience in enterprise, platform, software or cloud architecture, with a proven track record of designing large-scale enterprise platforms.
• At least 3 years of experience designing AI, Machine Learning or Generative AI platforms within an enterprise environment.
• Strong expertise in enterprise platform architecture, cloud-native technologies, distributed systems, microservices, event-driven architectures and API-first design.
• Solid understanding of enterprise AI platforms, foundation models, LLMs, SLMs, multimodal AI, Model-as-a-Service (MaaS) and agentic AI.

Responsibilities

• Define and maintain the enterprise AI platform architecture, reference architectures, technology roadmap and architectural standards to support scalable and consistent AI adoption.
• Assess and recommend foundation models, LLMs, SLMs, multimodal AI technologies and emerging AI frameworks, including hosted, self-hosted and open-weight models.
• Lead proof-of-concept initiatives, technology evaluations and pilot projects to validate new AI capabilities, optimise model performance and identify opportunities for platform enhancement.
• Establish architectural best practices for AI model deployment, evaluation, versioning, monitoring, retraining, performance optimisation and retirement.
• Drive the adoption of enterprise AI patterns, including RAG, agentic AI, workflow orchestration and API integrations.
• Define reusable design patterns, technical guidelines and architectural guardrails. Collaborate with Enterprise Architecture, Cybersecurity and governance teams to ensure alignment with enterprise standards, Responsible AI principles, data privacy and regulatory requirements.
• Partner with Engineering and Operations teams to establish platform observability, resilience, scalability, performance and lifecycle management standards for production AI services.
• Present and justify architecture proposals to senior management and architecture review boards. Provide technical direction to engineering teams and mentor architects and senior engineers.

Skills

Enterprise architecture

Cloud-native

Distributed systems

Microservices

API-first design

Stakeholder management

Leadership

Mentoring

Observability and governance

Education

Bachelor's degree in Computer Science or related field

Tools

Kubernetes

Cloud platforms (AWS/Azure/GCP)

Job description

We are seeking an experienced AI Platform Architect to drive the architecture, technology strategy and roadmap for AI platform. This role will play a pivotal part in building a secure, scalable and enterprise-ready AI ecosystem that accelerates AI adoption and innovation across the organization.

Key Responsibilities

• Define and maintain the enterprise AI platform architecture, reference architectures, technology roadmap and architectural standards to support scalable and consistent AI adoption.
• Assess and recommend foundation models, LLMs, SLMs, multimodal AI technologies and emerging AI frameworks, including hosted, self-hosted and open-weight models, based on business needs, technical suitability, security and cost considerations.
• Lead proof-of-concept initiatives, technology evaluations and pilot projects to validate new AI capabilities, optimise model performance and identify opportunities for platform enhancement.
• Establish architectural best practices for AI model deployment, evaluation, versioning, monitoring, retraining, performance optimisation and retirement. Drive the adoption of enterprise AI patterns, including RAG, agentic AI, workflow orchestration and API integrations.
• Define reusable design patterns, technical guidelines and architectural guardrails. Collaborate with Enterprise Architecture, Cybersecurity and governance teams to ensure alignment with enterprise standards, Responsible AI principles, data privacy and regulatory requirements.
• Partner with Engineering and Operations teams to establish platform observability, resilience, scalability, performance and lifecycle management standards for production AI services.
• Present and justify architecture proposals to senior management and architecture review boards. Provide technical direction to engineering teams, mentor architects and senior engineers, and collaborate with AI Solution Architects to enable reusable and scalable AI solutions.

Requirements

• Degree in Computer Science, Computer Engineering, Software Engineering, Information Systems or a related technical discipline.
• At least 10 years of experience in enterprise, platform, software or cloud architecture, with a proven track record of designing large-scale enterprise platforms.
• At least 3 years of experience designing AI, Machine Learning or Generative AI platforms within an enterprise environment.
• Strong expertise in enterprise platform architecture, cloud-native technologies, distributed systems, microservices, event-driven architectures and API-first design.
• Solid understanding of enterprise AI platforms, foundation models, LLMs, SLMs, multimodal AI, Model-as-a-Service (MaaS) and agentic AI.
• Experience with AI application architectures and patterns such as Retrieval-Augmented Generation (RAG), AI agents, workflow orchestration, Model Context Protocol (MCP) and enterprise integrations.
• Knowledge of AI model lifecycle management, including model evaluation, fine-tuning, deployment, versioning, monitoring and governance.
• Familiarity with hybrid cloud, on-premises architectures, container orchestration, AI security, Responsible AI, data governance and privacy requirements.
• Proven experience developing technology roadmaps, reusable architecture frameworks and technical standards, as well as evaluating emerging technologies and Build, Buy or Partner strategies.
• Strong stakeholder management, communication and presentation skills, with experience engaging senior management and architecture governance boards.
• Demonstrated technical leadership and mentoring capabilities, with the ability to influence architectural decisions and drive continuous improvement.