Chief Technology Officer – Enterprise AI Start-up

About the Company
Our client is an early-stage deep technology startup headquartered in Singapore, commercialising cutting-edge artificial intelligence research developed at one of Asia's leading universities. The company is building foundational AI infrastructure that enables organisations to securely retain, retrieve and reason over enterprise knowledge, allowing AI systems to deliver more accurate, context-aware and privacy-preserving outcomes.
The company is looking for a hands-on CTO to lead their technology strategy and technical execution. You will work closely with the founders, research collaborators, and engineering team to translate advanced AI capabilities into dependable products.
This role combines AI algorithm development, architecture design, team leadership, and customer engagement. You should be comfortable building critical components yourself, guiding a small team, and taking responsibility for delivery from early prototypes through production deployment.

About the Role
This role is ideal for someone who is entrepreneurial, hands-on and motivated to build in an early-stage startup.

Key Responsibilities
Own the technical roadmap and architecture. Translate business priorities and customer needs into a focused roadmap. Make sound decisions about architecture, model selection, infrastructure, and what to build internally versus adopt from existing technologies.
Lead AI product development. Guide the development of AI memory, multimodal intelligence, harness engineering, and agents that use tools and execute workflows. Build reusable capabilities that strengthen Synvo’s core platform across customer projects. Integrate recursive self-improvement (RSI) into the AI product development Cycle.
Stay hands-on. Prototype, write and review code, troubleshoot difficult issues, and help the team resolve technical bottlenecks. Set practical standards for software quality, testing, documentation, and deployment.
Establish rigorous evaluation. Define benchmarks and acceptance criteria for accuracy, reliability, latency, and cost. Use real customer tasks, failure analysis, and production feedback to prioritise improvements.
Connect research with commercial outcomes. Evaluate emerging methods and turn promising research into measurable product improvements, balancing experimentation with delivery commitments and available resources.
Build and lead the team. Recruit and mentor engineers, establish clear ownership, and coordinate full-time staff, part-time contributors, interns, and research collaborators. Work closely with the software engineering lead to align AI development with platform engineering and delivery.
Work directly with customers and partners. Understand business workflows, assess technical feasibility, scope proofs of concept, and guide successful pilots into production. Support technical discussions with customers, partners, and investors.
Deliver enterprise-ready systems. Oversee integration with customer data and business applications, with appropriate access controls, privacy protection, evidence traceability, auditability, monitoring, and operational support. Guide cloud, onpremises, and local deployment choices according to customer requirements.

Must-Have Requirements
Practical experience developing and deploying LLM-based systems, including RAG, document intelligence, or agentic workflows.
A strong record of building and shipping production software, with ownership of architecture and delivery.
Strong coding ability and depth in backend systems, APIs, data pipelines, databases, and deployment infrastructure.
Experience leading or mentoring engineers, setting priorities, and delivering through a small team with varied experience levels.
An understanding of enterprise requirements, including security, permissions, integrations, reliability, and maintainability.
Good technical and commercial judgment: able to balance product quality, speed, cost, and long-term platform development.
Clear communication skills and the ability to explain technical choices to customers and non-technical colleagues.
Comfort with an early-stage environment, substantial ownership, and regular handson Implementation.

Bonus additional experience
Research experience in foundation models, AI memory, search and retrieval, OCR, multimodal document processing, or agentic AI systems.
Experience in local models, on-device inference, model optimisation, or privacysensitive deployments.
Python and Java/Spring-based systems.
Taking enterprise AI pilots into sustained production use.
Translating research into products or working closely with academic research teams