This role ensures consistent execution of IT projects, operational support, and change management from planning through implementation, while supporting business expansion and ongoing IT operations at the BU level.
The role acts as the regional authority for IT standards, delivery discipline, and operational performance, ensuring alignment across all BUs without direct involvement in software development.
On-Site Client Deployment: Work directly at the client’s designated location in KL City Centre to execute cloud infrastructure projects, deliver system integration, and manage technical client requirements.
Cloud Infrastructure Provisioning: Design, deploy, and maintain secure, highly available cloud environments (AWS, Azure, or GCP) aligned with client security and operational policies.
Automation & IaC: Write and manage clean Infrastructure as Code (IaC) using tools like Terraform, CloudFormation, or Bicep to automate provisioning and minimize manual configuration.
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Build and lead the project delivery function: hire, mentor, and manage project managers; set delivery standards, playbooks, and KPIs.
Own portfolio-level delivery — timelines, budgets, quality — across all concurrent AI Vision, system integration, and enterprise deployment projects.
Personally lead the largest, most complex programs: CCTV / video surveillance rollouts, multi-site on-premise server and software deployments, and agentic AI initiatives (multi-agent orchestration systems, natural-language query layers over real-time vision event data, LLM tool-use and automation).
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Agentic AI ArchitectureDesign and implement production-grade agentic systems using models such as Claude. Agent orchestration, tool calling, function calling, multi-agent architectures, planning and task decomposition, agent memory, context management, state machines and workflow engines, long-running agents, human-in-the-loop systems, autonomous execution, recovery and retry mechanisms, observability, and evaluation. You understand the difference between LLM → Agent → Workflow → Autonomous System, and when each is appropriate.
Agentic Loops & Self-OptimizationA major part of the role is building closed-loop systems: Goal → Plan → Execute → Observe → Evaluate → Learn → Re-plan → Execute. Systems that evaluate their own outputs, detect failed actions, identify root causes, adjust strategies, optimize prompts and tool selection, keep what works, roll back what does not, and improve over time. Reflection, critique, self-evaluation, feedback loops, reward signals, evaluation frameworks, automated experimentation, memory, retrieval, state management.
Claude / LLM EngineeringDeep practical experience with Claude/Anthropic APIs is highly desirable. Tool use, structured outputs, streaming, context management, prompt engineering, system prompts, long-context workflows, model routing, token optimization, latency and cost optimization, context compression, agent memory, LLM evaluation. Experience with other frontier models (OpenAI, Gemini, Llama or equivalent) is a plus.
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