IT Infra & AI Manager / Section Manager – GenAI/ML, Hybrid Infrastructure & Security (IT & OT)

Role Purpose

Own end‑to‑end delivery of hybrid IT infrastructure, IT/OT cybersecurity, and the GenAI/ML platform foundation. Lead strategy, operations, governance, and teams to ensure secure, reliable, and scalable technology aligned to business outcomes.

• Clear IT strategy and roadmap aligned to business priorities
• Stable hybrid platform (cloud + on‑prem) with strong SLA/SLOs, observability, and cost control
• Improved IT & OT security posture, audit results, and incident response speed
• Mature AI governance (responsible AI, privacy, model risk, auditability)
• GenAI/ML solutions live in production with measurable ROI, productivity, and risk reduction

1. Hybrid IT Infrastructure Ownership

• Servers, virtualization, storage, backup/DR (on‑prem & cloud)
• Network (WAN/LAN/WLAN, segmentation, SD‑WAN/VPN, DNS/DHCP, firewalls, SASE)
• M365 & identity (tenant governance, Entra ID/Azure AD, Exchange, SharePoint, Intune)
• End‑User Computing (devices, imaging, patching, endpoint compliance)
• Operational excellence: monitoring, incident/problem/change management, BCP/DR testing
• Capacity planning, performance optimization, asset inventory, cost optimization
• Standardized reference architectures for hybrid and AI workloads

1. Cybersecurity Leadership (IT & OT)

• Security architecture: zero trust, IAM hardening, encryption, key management, segmentation
• DevSecOps: secure SDLC, threat modeling, scanning, SBOM, container/image security
• OT security: segmentation, access control, monitoring, risk assessments aligned to ops constraints
• Vulnerability & patch management governance with SLAs and reporting
• Lead incident response across AI, IT, and OT (RCA and preventive controls)

1. GenAI & ML Strategy and Delivery

• Define and execute GenAI/ML roadmap aligned to business value
• Own full use‑case lifecycle: discovery → data readiness → build/buy → deploy → monitor
• Apply GenAI patterns: RAG, agentic workflows, guardrails, HITL, evaluation frameworks
• Production standards: versioning, release gates, rollback, monitoring
• Manage AI vendors and contracts (LLMs, vector DBs, MLOps, security)

1. Governance, Risk, Compliance & Responsible AI

• AI governance: responsible AI, bias/quality checks, explainability, audit trails
• Privacy‑by‑design: data classification, access control, retention, DLP, third‑party risk
• Executive reporting: risk register, controls coverage, audit readiness

1. Strategy, Stakeholder & People Leadership

• Define target‑state architecture and 12–24 month roadmap
• Own budgets, investment cases, and vendor contracts
• Lead and develop infrastructure, AI/MLOps, and security teams (direct/matrix)
• Communicate trade‑offs on risk, speed, and cost to senior stakeholders
• Partner with Business, Data/AI, IT Ops, OT, Security, Legal, and Procurement

• Environment: Hybrid cloud + on‑prem; regional (ASEAN, China, India)
• Travel: Occasional regional travel

• Degree in Computer Science, Engineering, AI/Data, Cybersecurity, or equivalent experience
• 10+ years in technology leadership spanning infrastructure, security, and AI/ML
• 5+ years in manufacturing, industrial, or operations environments
• Proven delivery of AI solutions into production with monitoring and support
• Strong knowledge of GenAI/LLMs, hybrid infrastructure, and core security principles
• Familiar with cloud management, such as Azure portal, AWS Console, etc.

Preferred Skills:

• IT/OT security and cross‑domain segmentation programs
• AI/model risk and governance frameworks
• Certifications: Cloud, CISSP/CISM/CCSP, ITIL, networking/security