AI Senior Rollout and Support Manager

💼 Role Overview

As an AI Senior Rollout and Support Manager, you'll lead the design and deployment of end-to-end AI solutions that are robust, scalable, and span data ingestion, model deployment, API orchestration, and business system integration across hybrid cloud and on-premises environments. This role offers an exciting opportunity to grow your expertise in AI platform development while making a meaningful impact on our organization's digital transformation.

🎯 What you will do (Key Responsibilities)

• Lead the planning, testing, and delivery of solutions for the MA Global AI self-service platform, including Agentic AI, AI Workbench and Tooling, Model Management, shared RAG service, MCP, and AI Runtime environments
• Develop solution blueprints by translating business requirements into technical architecture, gaining hands-on experience in selecting appropriate tools, frameworks, and infrastructure for AI model development and operations
• Integrate AI capabilities with internal APIs, enterprise platforms, and user-facing applications in IT and Networks, working with LLM-based and agentic workflows
• Collaborate with security and governance teams to ensure solutions are secure and compliant, embedding PDPA and enterprise policy requirements into your designs
• Partner with MA AI and data teams to operationalize AI models, learning how to ensure architectural alignment, scalability, and lifecycle support
• Contribute to proof-of-concepts (PoCs), technical evaluations, and prototyping efforts, gaining valuable experience in emerging AI technologies
• Stay current with AI technologies and best practices in integration, model lifecycle management, and platform operations
• Participate actively in architecture reviews, technical discussions, and sprint planning with cross-functional teams
• Define and enforce architectural standards, reusable design patterns, open standards, and reference implementations to streamline AI deployment across business units
• Explore emerging AI technologies such as vector databases, context-aware agents, and orchestration protocols (e.g., LangChain, LangGraph, MCP, A2A), and assess their applicability within the enterprise
• Lead solutioning activities and mentor a small development team consisting of AI engineers and application developers on selected use cases
• Own the business knowledge strategy for AI, including documents, data, FAQs, and rules; coordinate with data owners and regions for knowledge onboarding
• Drive improvements in AI quality through prompt optimization, knowledge management, and feedback loops
• Support the auto-learning framework and guide the organization in adopting the self-service AI Framework and API interface