[Hanoi] Chief AI Officer (CAIO)

JOB DESCRIPTION

1. AI Strategy, Investment & Value Portfolio

• Develop and execute a 3–5 year AI strategy aligned with LPBank’s business strategy, digital transformation, risk appetite, and AI maturity level; define an AI-first vision, priority domains, and operating model (centralized/federated – hub & spoke).
• Establish a prioritized portfolio of use cases based on financial impact, technical/data feasibility, and risk level; integrate GenAI and Agentic AI into the roadmap.
• Develop a Build – Buy – Partner strategy, budget plans, stage-gate funding mechanisms, and resource allocation; manage AI costs (FinOps: GPU/token/inference costs) and return on investment (ROI).
• Proactively terminate, adjust, or reallocate resources for initiatives that fail to meet targets.

1. AI-Driven Business & Operations Transformation

• Partner with business units to redesign processes, products, and customer experiences powered by AI: Customer 360 analytics, personalization, next-best-action recommendations, virtual assistants/conversational banking, and AI assistants for Relationship Managers (RMs).
• Apply AI in credit (scoring, underwriting, early warning, debt collection), fraud prevention, anti-money laundering (AML), and financial crime (including deepfake and biometric spoofing detection), as well as real-time transaction monitoring.
• Intelligently automate operations, post-audit, compliance, legal, human resources, and coding assistance (AI for software development); deploy AI agents with human oversight and control (human-in-the-loop).
• Ensure solutions are integrated into business workflows, adopted by users, and deliver measurable improvements.

1. AI Platform & AI-Ready Data

• Direct the architecture and roadmap of shared AI platforms (model hub, RAG, vector database, AI Gateway, AI agent builder tools, feature store) in coordination with the IT Division; determine deployment models (on-premise/private cloud/public cloud) in compliance with data sovereignty, information security, and cost requirements.
• Lead MLOps/LLMOps/AgentOps capabilities: model versioning, testing, deployment, monitoring, updating, and retirement; define model strategy (open-source models, commercial models, Vietnamese language models, fine-tuning) and optimize compute capacity (GPU).
• Coordinate with the data governance unit to ensure AI data is high-quality, traceable, properly authorized, and compliant with personal data protection regulations; propose requirements for AI-ready data products. Enterprise data governance responsibilities remain with the assigned unit per the RACI matrix.
• Promote the reuse of shared components, minimize duplicate investments, and control operational costs.

1. Responsible AI Governance & AI Risk Management

• Establish, operate, and maintain a bank-wide AI governance framework compliant with laws, State Bank of Vietnam (SBV) regulations, and international standards (ISO/IEC 42001, NIST AI RMF, MAS FEAT principles): usage principles, risk tiering, approval authorities, accountability, and lifecycle controls.
• Build and maintain an inventory of all AI models and applications, including third-party AI and employee-used GenAI (shadow AI); ensure impact assessments, testing, independent validation, and approval prior to deployment, along with periodic operational monitoring.
• Collaborate with Risk Management, Compliance, Legal, IT, and Information Security to control bias/fairness, reliability, explainability, hallucination, data leakage, vendor risk, and customer rights affected by automated decisions; ensure human oversight and intervention mechanisms are in place.
• Establish mechanisms for detecting, reporting, responding to, and remediating AI incidents; define conditions for restricting, suspending, or retiring models.
• Prepare documentation and work with regulators and auditors regarding AI matters within the scope of responsibility.

1. AI Safety, Security & Cybersecurity

• Collaborate with the Information Security department to establish security standards for AI/GenAI systems: defense against prompt injection, data leakage, data/model poisoning, model theft, and AI agent tool misuse; conduct pre- and post-deployment AI red teaming.
• Apply AI to cybersecurity and anti-fraud (detecting deepfakes, voice/biometric spoofing, and anomalous behaviors); prepare response plans against AI-powered attacks.
• Manage third-party risks, vendor concentration (vendor lock-in), and cross-border data risks; ensure contingency plans, vendor portability, and operational continuity for critical AI services.

1. AI Organization, Talent & Culture Development

• Build the AI organizational model, staffing structure, and capability development plan aligned with LPBank’s strategy and AI maturity level.
• Attract, recruit, develop, retain, and lead a team of AI specialists, data scientists, AI/ML engineers, and AI governance experts; establish competitive compensation mechanisms and career paths for tech talent.
• Roll out bank-wide AI awareness and upskilling programs tailored by target groups; lead change management and job/role redesign as AI augments human workers.
• Establish a bank-wide Responsible AI Usage Policy for all employees.

1. Perform other assigned duties

JOB REQUIREMENTS

1. Education & Qualifications:

• Master’s degree or higher in Computer Science, Artificial Intelligence, Mathematics–Informatics, Statistics, Data Science, Engineering, or related fields from reputable domestic or international universities.
• English: Level 3 or higher (IELTS 7.0+ or equivalent); preference for candidates proficient in presenting, negotiating, and working directly with international partners/experts at the executive level.
• Preference for candidates holding ISO/IEC 42001 (Lead Implementer/Auditor), AIGP (IAPP), or equivalent AI governance certifications; advanced cloud AI/ML certifications (AWS/Azure/GCP); or information security certifications (CISA/CISSP/CISM).
• Office IT: Proficient in Word, Excel, and PowerPoint; proficient in utilizing AI/GenAI tools in daily work.

1. Experience:

• Professional experience: Minimum of 12 years working in Data, Advanced Analytics, and AI/ML, including at least 5 years directly leading the deployment of AI/ML/GenAI solutions into production within banking, finance, or large technology enterprises.
• Management experience: Minimum of 08 years in senior management roles, including at least 3 years leading a large-scale AI, data, or technology division/unit (25–30+ staff and/or substantial investment budget) at a commercial bank, Fintech, or technology corporation of equivalent scale.
• Mandatory:
• Proven track record of deploying AI/ML solutions into large-scale production that generated measurable business value.
• Experience managing technology investment budgets, teams of AI/data science/engineering specialists, and working with the Board of Directors (BOD) / Executive Board.
• Deep understanding of the compliance and risk management environment in the banking and finance sector.
• Preferred:
• PhD degree or scientific publications/patents in AI/ML; executive education or MBA in AI strategy and governance.
• Experience successfully deploying LLMs, Generative AI/Agentic AI in a bank/financial institution or a reputable international/domestic tech lab; having led an enterprise-wide AI transformation program.
• Experience at leading banks/financial institutions in Southeast Asia or globally; possession of an expert and partner network within the AI ecosystem.
• Practical understanding of AI deployment in the Vietnamese market (Vietnamese language data, customer behavior, infrastructure).

1. Knowledge:

• Banking Operations & Market: Deep understanding of the ecosystem, business models, structure, and culture of Vietnamese commercial banks (funding, credit, payments, capital markets, retail/corporate banking); solid grasp of risk management (credit, operational, fraud, financial crime), digital financial behavior in Vietnam, and banking AI trends in Southeast Asia and globally.
• AI Architecture & Operations: Proficient in Transformers, LLMs, SLMs, multimodal models, RAG, multi-agent systems (and agent protocols), RLHF, and evals; Data Mesh/Data Product infrastructure, Feature Store, Lakehouse, distributed computing (Spark, Ray, K8s), GPU/inference optimization (quantization, fine-tuning, distillation), and hybrid cloud/on-premise architectures; operations of MLOps/LLMOps/AgentOps, AI Gateway, and FinOps.
• AI Applications & Risk Governance: Experience deploying AI in banking (credit, FCC/AML/fraud, Customer 360, personalization, CX, hyperautomation); solid grasp of Model Risk Management (MRM), Responsible AI (fairness, transparency, explainability), and AI security (OWASP LLM Top 10, red teaming).
• Vietnamese Regulations & International Standards: Well-versed in the Law on Artificial Intelligence (effective March 1, 2026), Personal Data Protection Law, Data Law, Law on Digital Technology Industry, Cybersecurity Law, Law on Credit Institutions, current SBV regulations (information security, IT risk, biometrics, consumer protection), as well as international standards (ISO/IEC 42001, NIST AI RMF, EU AI Act, MAS FEAT, Basel/FSB).

1. Skills:

• Core Competencies: Digital capability, Change management, Risk management and compliance.
• Functional Competencies:
• Strategic direction and AI investment decision-making, linking technology to business value and P&L.
• Driving AI innovation/transformation; scaling AI from pilot to enterprise-wide production.
• Governing AI risk, safety, and ethics; working effectively with regulators and control functions.
• Building an enterprise-wide AI culture and capability; attracting and retaining tech talent.
• Influencing, negotiating, and communicating with the BOD, Executive Board, regulators, and partners; cross-functional collaboration without direct authority.
• Management / Leadership Competencies: Strategic thinking and vision; Fostering innovation and creativity; Talent development and coaching.