Chief Technology Officer (AI Startup)

talentweb3; Singapore; On-site; SGD 300,000 - 600,000; Full time

Job summary

TalentWeb3 is seeking a hands-on CTO to shape technology strategy and oversee execution from prototypes to production. You will collaborate with founders, researchers, and engineers to translate AI capabilities into dependable products and scalable platform features.

You will prototype, review code, mentor engineers, and drive delivery while balancing customer engagements and research-driven innovation. This role requires strong architectural judgment and end-to-end ownership.

Qualifications

• Practical experience developing and deploying LLM-based systems, including RAG, document intelligence, or agentic workflows.
• Strong ownership of architecture and delivery in production software.
• Experience leading engineers and delivering through a small team with varied experience levels.
• Understanding of security, permissions, integrations, reliability, and maintainability.
• Clear communication to customers and non-technical colleagues.

Responsibilities

• Own the technical roadmap and architecture; translate priorities into a focused plan.
• Lead AI product development across memory, multimodal intelligence, tools, and RSI integration.
• Stay hands-on: prototype, code review, troubleshoot bottlenecks, set quality standards.
• Define benchmarks for accuracy, latency, and cost; use production feedback to improve.
• Bridge research and commercial outcomes; turn ideas into measurable product gains.
• Build and lead the team; hire, mentor, and align AI with platform delivery.
• Engage with customers and partners; scope pilots and shepherd them to production.
• Deliver enterprise-ready systems with proper access controls, auditing, and monitoring.

Skills

Backend systems

APIs

Data pipelines

Databases

Deployment infra

Leadership

Product delivery

Technical decision‑making

Customer engagement

Tools

Python

Java/Spring

Job description

The role

We are looking for a hands‑on CTO to lead our 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.

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 our 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, on‑premises, and local deployment choices according to customer requirements.

What we are looking for

• 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 hands‑on implementation.

Useful 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 privacy‑sensitive 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.

What success looks like

A clear and executable technical roadmap; reliable customer deployments; measurable improvements in AI quality and efficiency; reusable technology across projects; and a capable engineering team with clear accountability.