Chief Technology Officer & Chief AI Officer (CTO & CAIO)

Key Responsibilities

1. Global Software Standard — at scale and speed

-Establish a lightweight, repeatable engineering system across products and delivery teams: coding standards, peer review, automated tests, secure development, CI/CD, release quality gates, observability, and documentation.

- Improve delivery throughput without sacrificing customer experience or production stability. Use lead time, deployment frequency, change failure rate, recovery time, escaped defects, and user-impact measures together.

-Build reusable starter kits, shared components, API contracts, infrastructure modules, and paved-road delivery workflows. Define justified exceptions rather than forcing one architecture onto every product.

- Introduce AI-assisted engineering with scoped tasks, repository context, test and evaluation harnesses, human review, dependency checks, and traceable changes. Measure accepted working output, not generated code volume.

-Identify bottlenecks and improve processes systematically through baseline → hypothesis → experiment → measurement → standardization.

2. Architecture & technical decision-making

-Own application, platform, integration, data, and AI architecture. Translate business goals and product journeys into clear boundaries, contracts, deployment patterns, and non-functional requirements.

-Design for multi-tenancy, data isolation, growth, performance, resilience, maintainability, and international expansion; balance today's simplicity with tomorrow's migration path.

-Make explicit build-versus-buy and managed-versus-self-hosted decisions. Compare time-to-market, total cost, operational burden, lock-in, reversibility, and differentiated value.

-Use architecture decision records, threat models, capacity models, and technical debt priorities to make trade-offs reviewable and understandable to non-technical leaders.

-Remain hands-on in high-leverage work: prototype architecture risks, review important pull requests, pair on difficult engineering problems, and diagnose production failures

3. Agentic AI & Harness Engineering

- Build production agents that use tools, reason over business context, coordinate workflows, and complete useful tasks with measurable results.

-Own the harness around the model: orchestration, tool contracts, state and memory, context management, permissions, sandboxing, budgets, timeouts, retries, idempotency, durable execution, and recovery.

- Define human approval and escalation boundaries for consequential actions; defend against prompt injection, unauthorized tool use, cross-tenant retrieval, and unintended data exposure.

-Build offline evaluations, regression suites, adversarial tests, production traces, and feedback loops. Measure task completion, factual grounding, tool correctness, escalation quality, latency, and cost per successful task.

-Choose models and frameworks through representative benchmarks. Use deterministic workflows where they are more reliable; adopt multi-agent designs only when their value outweighs added complexity.

4. AI & Technology Infrastructure

- Own cloud, data platforms, model-serving or provider integration, storage, queues, identity, secrets, deployment environments, and developer infrastructure.

-Build practical DevSecOps, infrastructure-as-code, access controls, audit trails, backups, tested restores, disaster recovery, and incident response.

-Define customer-relevant SLOs, error budgets, RTO/RPO, capacity plans, and on-call ownership. Instrument the path from user request to agent/tool execution and business outcome.

-Manage model routing, caching, rate limits, concurrency, fallback, provider outages, and token economics. Match technical design to acceptable customer latency and cost.

- Make infrastructure spend visible by product and tenant; improve cost per successful workflow and gross-margin contribution as adoption grows.

-Support enterprise privacy, security, and governance expectations with Product and relevant specialists, while keeping the platform usable and delivery fast.

5. Global AI Tech Product Development

-Partner with Product, Design, Business, Sales, and Customer Success to discover valuable problems, prioritize opportunities, prototype solutions, and validate demand before scaling investment.

-Translate technology capabilities into differentiated experiences: clear onboarding, trustworthy AI interaction, visible progress, failure recovery, accessibility, and measurable time-to-value.

-Take end-to-end ownership of technology's contribution to product success: activation, meaningful usage, retention, willingness to pay, revenue, reliability, support burden, and unit economics.

-Lead products through 0 → 1 → 100 → 1,000 with stage-specific evidence and decision gates; be willing to simplify, pivot, or stop weak bets.

-Prepare for international markets through appropriate localization, identity, privacy/data-residency choices, billing integrations, supportability, and global performance — prioritized against validated demand.

-Build durable competitive advantages: reusable platforms, proprietary workflow knowledge, lawful and permissioned learning loops, integration ecosystems, and product assets that compound with use.

Qualifications

- Minimum 10 years of relevant professional experience in software engineering, architecture, technology product development, or closely related roles. This is total relevant experience, not 10 years of generative AI experience.

-Required startup experience: has built or scaled products in a startup, venture-building, or early-stage product environment with limited resources and high uncertainty.

-Required product-success evidence: has personally driven a product from inception to meaningful adoption and commercial results, then helped scale it. Explain the starting point, growth unit, business outcomes, personal decisions, team contribution, and lessons.

-Strong hands-on engineering capability: can still write, inspect, test, and debug production code; explain architecture trade-offs; and contribute directly to critical delivery work.

-Demonstrated architecture and infrastructure experience in production systems, including reliability, security, data, integrations, and scaling economics.

-Practical experience shipping AI-enabled products or agentic workflows beyond demos; understands evaluation, tool execution, failure handling, access boundaries, and operational cost. --Track record of leading engineers and cross-functional teams while improving delivery processes and developing other technical leaders.

-Strong product design judgment and commercial thinking: understands customer needs, usability, adoption, pricing hypotheses, margins, and opportunity cost.

-Able to communicate architecture, product choices, and business impact clearly in English for global collaboration.

Benefits:

We offer a competitive compensation and benefits package, including health insurance, paid time off, professional development opportunities, and the chance to work on cutting-edge solutions in a dynamic environment. You will be joining a talented and passionate team at the forefront of digital innovation in Thailand.

Why Join Us

• Wear two hats and make a significant impact on the development and success of Jenosize's marketing technology solutions.
• Leverage your project management and product ownership skills to ensure efficient delivery and user-centric product development.
• Work in a collaborative and fast-paced environment where innovation is encouraged.
• Be part of a team that is shaping the future of marketing technology in Thailand.

Ready to make a difference Apply today!