Chief Technology Officer (Enterprise AI solutions company)

Talentvis Singapore Pte Ltd.

Singapore

On-site

SGD 180,000 - 340,000

Full time

2 days ago
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Job summary

Talentvis Singapore Pte Ltd. is seeking a visionary CTO to lead our technology strategy, engineering organization, and AI product architecture for a fast-growing, product-led AI software company.

You will review production LLM pipelines, design scalable inference architectures, and guide an agile team from prototype to enterprise scale. You will partner with C-suite leaders to shape technical roadmaps, ensure data privacy and regulatory compliance, and represent capabilities to customers,

Qualifications

  • Proven track record in leading AI/ML product architecture and engineering teams.
  • Hands-on with production LLM-based systems and multi-model AI architectures.
  • Strong background in cloud-native infrastructure, APIs, and data pipelines.

Responsibilities

  • Own AI platform roadmap, model selection, fine-tuning, and inference optimization.
  • Lead architecture reviews, design reviews, and scale the engineering org.
  • Ensure data privacy, access controls, auditing, and regulatory compliance for enterprise clients.
  • Mentor a distributed engineering team, set performance metrics and standards.
  • Translate complex technical choices into business value for execs and partners.
  • Participate in customer pilots, due diligence, and vendor/build-vs-buy evaluations.

Skills

AI leadership
LLM systems
Cloud-native
Security

Job description

About the role

On behalf of our client (a fast-growing, product-led AI software company building next-generation enterprise intelligence and automation platforms), we are seeking a visionary, hands-on Chief Technology Officer (CTO) to lead our technology strategy, engineering organization, and AI product architecture. We need a technical leader who is comfortable rolling up their sleeves to review core LLM/AI model pipelines, design production inference architectures, and guide an agile team from prototype to enterprise scale. You will partner closely with the other C-suite level leaders to shape company direction, manage technical roadmaps, and represent our technical capabilities to key customers, partners, and investors.

Key responsibilities
  • Own the end-to-end AI platform roadmap, including model selection, fine-tuning, RAG frameworks, agentic workflows, inference optimization, and cloud infrastructure.
  • Remain deeply engaged in critical architecture reviews, code/system design, debugging, and high-stakes problem-solving while scaling the engineering team.
  • Ensure robust data privacy, access controls, auditability, regulatory compliance, and reliable deployment models (cloud, hybrid, or on-premise) for enterprise customers.
  • Scale, mentor, and lead a high-performing, distributed engineering and AI team; set clear performance metrics and engineering standards.
  • Translate complex technical choices into clear business value for the CEO, board members, and non-technical stakeholders.
  • Join strategic customer pilots, technical due diligence sessions with investors, and vendor/build-vs-buy evaluations.
About you
  • 8-12+ years in software engineering, technical infrastructure, or AI development, with at least 5 years in a senior executive or technical leadership role (CTO, VP of Engineering, or Head of AI).
  • Practical, hands-on experience designing and shipping production LLM-based systems, RAG pipelines, or multi-model generative AI architectures.
  • Strong coding foundation and depth in backend systems, APIs, cloud-native infrastructure, and modern database/data pipelines.
  • Deep understanding of enterprise software requirements, including security, SOC2/privacy frameworks, scalability, and high reliability.
  • Comfortable thriving in an early- to growth-stage environment where speed, adaptability, and direct execution matter.
  • Experience guiding a startup through funding rounds, technical due diligence, or significant platform scaling events (preferred).
  • Experience with local model deployment, on-device inference, or privacy-sensitive/ highly-regulated industry environments (preferred).

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