Chief Scientist / VP, AI (Singapore) - AI Strategy & Implementation Lead (Reports to CEO)
About the role
We are hiring a C-level AI leader to own company-wide AI strategy and drive end-to-end implementation-from research direction to productization and commercialization. This position reports directly to the CEO, with mandate to shape our digitalization roadmap, build the AI organization, and deliver measurable business outcomes.
Scope & expected outcomes:
1) Company AI + digitalization strategy
- Define the north-star AI strategy and multi-year digitalization roadmap aligned to business priorities.
- Identify where AI wins across product, operations, delivery, and customer experience translate into a sequenced execution plan with measurable ROI.
- Establish AI governance: architecture standards, model lifecycle, data strategy, security/privacy-by-design, and quality/reliability targets.
2) Requirements capture and delivery planning
- Partner with business owners to capture requirements, define value hypotheses, and prioritize initiatives.
- Convert requirements into deliverable roadmaps including milestones, staffing, budget, data readiness, risk controls etc..
- Create executive-ready artifacts: strategy memos, investment cases, and KPI dashboards.
3) Technology research, prototyping, and technical direction
- Track and evaluate state-of-the-art in foundation models, multimodal AI, retrieval/knowledge systems, agents, and system optimization.
- Lead rapid prototyping to validate feasibility, performance, cost, and deployment constraints.
- Make high-leverage architectural decisions: model selection, training/fine-tuning, inference optimization, edge/cloud split, and observability.
4) Commercialization and go-to-market enablement
- Productize capabilities into repeatable.
- Define packaging, pricing, and delivery models.
- Support strategic customers and partnerships represent the company externally such as conferences, customers, and ecosystem.
What we're looking for:
Must-have
- Deep computer systems background: distributed systems, high-performance computing, storage/data pipelines, and production reliability.
- Strong applied AI track record: shipping real products, owning accuracy, latency, cost, and reliability in production.
- Ability to operate at executive altitude and dive deep technically (architecture reviews, model tradeoffs, debugging).
- Proven experience leading teams (hiring, mentoring, performance management) and building org processes.
Distinguishers
- Rich academic and industry experience (e.g., PhD or equivalent research impact plus product leadership), with strong publication/patent/open-source or recognized industry contributions.
- Expertise in modern AI stacks: LLMs / multimodal models, RAG/knowledge retrieval, agentic workflows, evaluation frameworks, MLOps.
