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.
Key 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
Core Responsibilities
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
2) 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)
3) 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)
4) 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
5) 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
Scope & Interfaces
- Environment: Hybrid cloud + on‑prem regional (ASEAN, China, India)
- Travel: Occasional regional travel
Required Qualifications
- 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
