Founding Chief Technology Officer

Company Overview:

Our client is a technology company building a next-generation, AI-native enterprise platform for the global maritime and shipping industry. Its first product is being developed for a leading Singapore-based shipping company, with stakeholders across Singapore, India, Europe, and the United States. The platform will combine artificial intelligence, cloud computing, mobile and offline capabilities, automation, analytics, IoT, and modern enterprise architecture. Our vision is to build a globally scalable technology platform that transforms maritime operations and can support future enterprise products.

Position Summary:

The Founding CTO will be a key member of the leadership team and will define and execute the company's technology strategy. The CTO will be responsible for designing the AI-native platform architecture, selecting the technology stack, establishing engineering and cybersecurity standards, planning phased product delivery, and building a high-performing engineering organization. This role requires a hands-on strategic leader who can balance innovation, delivery speed, scalability, security, and cost.

Key Strategy and Architecture:

- Define the company's technology vision, architecture principles, and long-term roadmap.

- Design a secure, scalable, cloud-native and AI-first enterprise platform.

- Select the appropriate technology stack, cloud platform, databases, development frameworks, and AI technologies.

- Establish a platform-first architecture that supports phased delivery of individual ERP modules.

- Define mobile-first, web-based, API-first, event-driven, and offline-capable architecture.

- Ensure the platform supports scalability, high availability, low latency, disaster recovery, and global operations.

AI, Data and Innovation:

- Define the AI strategy and ensure AI is built into the platform from the beginning rather than added later.

- Plan capabilities such as enterprise AI assistants, intelligent workflows, predictive analytics, document intelligence, anomaly detection, and decision support.

- Define the architecture for LLMs, AI agents, enterprise knowledge, vector search, and secure data access.

- Establish AI governance covering accuracy, security, human approval, monitoring, compliance, and responsible use.

- Optimize AI performance and costs through appropriate model selection, model routing, caching, token controls, and usage monitoring.

- Evaluate emerging technologies and recommend build-versus-buy decisions.

Cloud, Integration and Cybersecurity:

- Define the cloud and infrastructure strategy across AWS, Azure, or GCP.

- Use serverless, containerized, and edge infrastructure appropriately based on workload requirements.

- Establish integration standards for legacy ERP systems, AIS, IoT sensors, finance platforms, document systems, and external services.

- Define API, data synchronization, and onboard-to-cloud connectivity standards.

- Establish cybersecurity policies covering identity, access control, encryption, audit trails, monitoring, threat detection, and disaster recovery.

- Ensure enterprise-grade security and compliance are incorporated throughout the development lifecycle.

Engineering Leadership and Delivery:

- Build and lead a multidisciplinary engineering organization across platform, backend, web, mobile, AI, data, integration, DevOps, security, and quality assurance.

- Establish engineering standards, development processes, architecture governance, testing, CI/CD, documentation, and release practices.

- Translate business requirements into practical, scalable technology solutions.

- Work closely with product, business, UX, maritime domain experts, and client stakeholders.

- Lead technical discussions during discovery, implementation planning, and executive reviews.

- Ensure measurable, usable capabilities are delivered through a phased implementation approach.

Team Planning and Cost Optimization:

- Develop a phased organization and hiring roadmap aligned with platform and module delivery.

- Identify which skills are required internally and which can be supported through specialist partners or contractors.

- Avoid premature hiring by expanding the team based on delivery priorities and business needs.

- Prepare technology budgets and monitor infrastructure, engineering, licensing, and AI operating costs.

- Optimize costs without compromising security, quality, scalability, or long-term maintainability.

- Establish transparent metrics for engineering productivity, cloud consumption, AI usage, and total cost of ownership.

Key Deliverables During the First Six Months:

- Technology vision and product roadmap.

- AI-native platform and reference architecture.

- Platform-first, phased implementation plan.

- Cloud, infrastructure, and disaster recovery strategy.

- Enterprise data, API, integration, and offline-sync architecture.

- AI strategy, model approach, governance, and cost-control framework.

- Cybersecurity and access-control framework.

- Engineering standards, DevOps model, and CI/CD pipeline.

- Phased team structure, hiring plan, and partner strategy.

- Initial technology budget and total cost-of-ownership framework.

- Architecture and delivery plan for the first usable business Bachelor's or Masters degree in Computer Science, Software Engineering, or a related field.

- 15+ years of software engineering experience, including senior technology leadership.

- Proven experience designing and delivering large-scale SaaS, ERP, or enterprise platforms.

- Strong expertise in cloud-native architecture, APIs, distributed systems, databases, DevOps, cybersecurity, and enterprise integration.

- Practical understanding of modern AI platforms, LLMs, RAG, AI agents, vector databases, analytics, and AI governance.

- Experience building mobile-first, offline-capable, or globally distributed applications.

- Experience planning and scaling multidisciplinary engineering teams in phases.

- Strong commercial understanding of technology investment, infrastructure costs, vendor selection, and build-versus-buy decisions.

- Maritime, shipping, logistics, transportation, manufacturing, or asset-intensive industry experience would be advantageous.

Key Competencies:

- Strategic technology leadership.

- AI-native product and platform thinking.

- Enterprise and solution architecture.

- Cloud, data, integration, and cybersecurity.

- Engineering team building and mentoring.

- Phased delivery and cost management.

- Product and stakeholder collaboration.

- Clear executive communication.

- Innovation with practical business judgment.

(ref:hirist.tech)