Head of Artificial Intelligence (Gen AI, Copilot AI, Agentic AI)
The Head of Artificial Intelligence leads the Group’s end-to-end AI strategy, delivery, and governance across Generative AI, Copilot AI (productivity AI), and Agentic AI (autonomous and tool-using agents). The role is accountable for value creation, safe‑by‑design engineering, and regulatory compliance across all jurisdictions in which the Group operates. This leader also serves as the CDO’s primary counterpart for the Data & AI Governance Control Tower—operationalizing policies, standards, risk controls, and regulatory obligations (BNM, national regulators, and international frameworks such as the EU AI Act and EU Data Act).
About the Role
Scope :
- Enterprise AI portfolio spanning Generative AI (LLMs, diffusion), Copilot AI for productivity, and Agentic AI (tool-using and workflow / decision agents).
- AI product engineering, MLOps and AIOps, evaluation, monitoring, and resilience.
- Data & AI Governance Control Tower dashboards and workflows across policy, standards, risk, compliance, and audit.
- Regulatory adherence : BNM technology risk (RMiT), PDPA (as amended), sectoral / national regulators, and international references (EU AI Act, EU Data Act).
- Change management, literacy, and adoption across Group business units and corporate functions.
Responsibilities
Strategy & Portfolio : Define a 3–5 year AI strategy and investment roadmap covering GenAI, Copilot AI, and Agentic AI; maintain an enterprise AI use‑case pipeline with quantified business value, risks, and ROI.AI Product Leadership : Stand up cross‑functional teams (AI product managers, data scientists, ML engineers, prompt / interaction engineers, evaluators) to ship AI products and agents that meet reliability, robustness, and safety thresholds.Agentic AI & Orchestration : Establish standards for agent architectures (planning, tools, memory, feedback, human‑in‑the‑loop); implement guardrails, fail‑safes, and escalation paths for autonomous actions.Copilot AI (Productivity AI) : Drive safe enablement of Copilot‑style assistants across collaboration suites; enforce identity, permissions, sensitivity labels, and DLP policies; instrument adoption, safety, and productivity metrics.GenAI Engineering Excellence : Govern patterns for model selection (open, hosted, and proprietary), fine‑tuning / RAG, prompt design, evaluation (hallucination, bias, toxicity), cost / performance optimization, and observability.Data & AI Governance Control Tower : Operationalize the Control Tower (platform and processes) to manage AI model inventory / registry, lineage, risk registers, DPIAs / AI impact assessments, policy attestation, and audit trails.Regulatory Compliance : Translate BNM RMiT requirements (governance, technology risk, cloud consultation / notification, cybersecurity), PDPA amendments, and international obligations (EU AI Act / Data Act) into actionable controls and evidence.Risk Management : Run pre‑deployment reviews; define go / no‑go criteria; manage incidents involving AI‑generated content or actions; institute post‑market monitoring for AI systems and agents.MLOps & AIOps : Implement standardized CICD for models / agents, model versioning, feature stores, evaluation pipelines, drift detection, human‑in‑the‑loop override, and rollback procedures.Security & Privacy : Enforce zero‑trust principles, least privilege, data minimization, encryption, red‑teaming (traditional and LLM‑specific), jailbreak / prompt‑injection defenses, and content provenance / watermarking where applicable.Ethics & Responsible AI : Embed fairness, explainability, transparency notes, and stakeholder engagement; maintain documentation (model cards, system cards, transparency notes).Change & Adoption : Build enterprise AI literacy programs; coach business units on use‑case delivery; define citizen‑developer guardrails and approval flows.Vendor & Partner Management : Oversee SI and platform partners; negotiate SLAs on safety, reliability, latency, uptime; ensure exit strategies and portability.Budget & KPIs : Manage P&L for AI portfolio; track KPIs (business impact, efficiency, reliability / safety, regulatory audit readiness, cost‑to‑value).Qualifications
Advanced degree in Computer Science, AI / ML, Data Science, or related field; or equivalent experience.12+ years in AI / ML and data leadership; 4+ years delivering GenAI / LLM applications and AI agents at enterprise scale.Hands‑on experience with model development (RAG, fine‑tuning), agent frameworks, evaluation, and MLOps.Demonstrated delivery in regulated environments; familiarity with technology risk, privacy, and compliance obligations.Proven team‑building and stakeholder management across business, risk, legal, security, and technology.Required Skills
AI Product Leadership and Portfolio Management.Risk‑based thinking and regulatory translation into controls and evidence.Technical depth in LLMs / GenAI, orchestration / agents, data platforms, and cloud.Operational excellence in MLOps / AIOps and reliability engineering.Excellent communication; ability to create executive‑ready materials and transparency notes.Preferred Skills
ISO / IEC 42001 (AI Management System) knowledge or implementation experience.Cloud certifications (Azure / AWS / GCP) relevant to AI workloads.Privacy / security certifications (e.g., CIPP / E, CIPM, CISSP) are a plus.#J-18808-Ljbffr