{"id":6290,"date":"2026-10-05T15:46:08","date_gmt":"2026-10-05T07:46:08","guid":{"rendered":"https:\/\/www.thousandsofyearsmedia.com\/?post_type=career&p=6290"},"modified":"2026-10-06T09:30:03","modified_gmt":"2026-10-06T01:30:03","slug":"ai-software-engineer-ai-native-development","status":"publish","type":"career","link":"https:\/\/www.thousandsofyearsmedia.com\/zh\/career\/ai-software-engineer-ai-native-development\/","title":{"rendered":"AI Software Engineer (AI-Native Development)"},"content":{"rendered":"
\n

\n <\/h1>\n<\/section>\n
\n\t\t\t\t\"AI-Native\n\t<\/figure>\n

\n\t\t\t\t<\/h2>\n\n
\n

\n Role Overview <\/h3>\n

AI & Product Technology<\/strong><\/p>\n

Build With AI. Build AI Into Products. Turn New Technology Into Real Business Capability.<\/strong><\/p>\n

Company: Thousands of Years Media Sdn. Bhd. (ToYm)<\/strong><\/p>\n

    \n
  1. ROLE MISSION<\/strong><\/li>\n<\/ol>\n

    At Thousands of Years Media Sdn. Bhd. (ToYm)<\/strong>, we are looking for an AI Software Engineer (AI-Native Development)<\/strong> who can build real applications, features and automation where AI is a core part of the product or workflow.<\/p>\n

    You may work across BiteShorts, other ToYm applications, internal systems and new AI-driven products<\/strong>.<\/p>\n

    Your mission is not simply to use AI tools.<\/p>\n

    Your mission is to:<\/p>\n

    Identify \u2192 Prototype \u2192 Build \u2192 Evaluate \u2192 Deploy \u2192 Monitor \u2192 Improve \u2192 Scale<\/strong><\/p>\n

    AI-powered systems that create measurable value.<\/p>\n

    This may include:<\/p>\n

    AI Applications \u2022 AI Agents \u2022 Workflow Automation \u2022 LLM-Powered Features \u2022 Search & Discovery \u2022 Content Systems \u2022 Recommendation \u2022 Internal Productivity Tools \u2022 Data Processing \u2022 Customer Experiences \u2022 AI-Assisted Operations<\/p>\n

    We need someone who understands both AI and software engineering<\/strong>.<\/p>\n

    A successful prototype is only the beginning.<\/p>\n

    It must eventually become a system that is reliable, secure, maintainable and useful.<\/p>\n

      \n
    1. WHY THIS OPPORTUNITY IS DIFFERENT<\/strong><\/li>\n<\/ol>\n

      AI is changing both:<\/p>\n

      What software can do<\/strong><\/p>\n

      \u4e0e<\/p>\n

      How software is built<\/strong><\/p>\n

      At ToYm, you will work on both.<\/p>\n

      You will use AI-native development methods to increase engineering speed while also embedding AI into real products and business workflows.<\/p>\n

      You may work across entertainment, digital platforms, commerce, content, creator ecosystems and internal operations.<\/p>\n

      We want someone who can move from:<\/p>\n

      Problem \u2192 AI Opportunity \u2192 Prototype \u2192 Evaluation \u2192 Production \u2192 Measurable Outcome<\/strong><\/p>\n

      -not someone who only experiments with prompts or creates demonstrations.<\/p>\n

      \n Job Description <\/h3>\n
        \n
      1. WHAT YOU WILL OWN<\/strong><\/li>\n<\/ol>\n

        A. Build AI-Powered Products<\/strong><\/p>\n

          \n
        • Build applications where AI is a core capability.<\/li>\n
        • Integrate AI models and APIs into production software.<\/li>\n
        • Develop AI-assisted user experiences.<\/li>\n
        • Turn prototypes into maintainable systems.<\/li>\n
        • Evaluate whether AI is actually the right solution before implementing it.<\/li>\n<\/ul>\n

          B. Build LLM & Multimodal Features<\/strong><\/p>\n

          Work with capabilities such as:<\/p>\n

            \n
          • Large Language Models<\/li>\n
          • Multimodal AI<\/li>\n
          • Structured Outputs<\/li>\n
          • Tool \/ Function Calling<\/li>\n
          • Embeddings<\/li>\n
          • Semantic Search<\/li>\n
          • Retrieval-Augmented Generation (RAG)<\/li>\n
          • AI Agents<\/li>\n
          • Image, Video, Audio or Text AI services where required<\/li>\n<\/ul>\n

            Choose solutions based on quality, reliability, latency, cost and business value<\/strong>.<\/p>\n

            C. AI Agents & Automation<\/strong><\/p>\n

              \n
            • Identify repeatable processes that can be automated.<\/li>\n
            • Build agentic and workflow-based systems.<\/li>\n
            • Connect AI with APIs, databases and business tools.<\/li>\n
            • Implement human review where full automation is inappropriate.<\/li>\n
            • Measure whether automation improves speed, quality, productivity or cost.<\/li>\n<\/ul>\n

              D. Build Complete Software Around AI<\/strong><\/p>\n

              AI alone is not a complete product.<\/p>\n

              You should be capable of building or contributing to:<\/p>\n

              Frontend \u2022 Backend \u2022 APIs \u2022 Databases \u2022 Authentication \u2022 Integrations \u2022 Cloud Deployment \u2022 Monitoring<\/strong><\/p>\n

              Build complete systems where AI works as part of a reliable software architecture.<\/p>\n

              E. Reliability, Security & Guardrails<\/strong><\/p>\n

              Design for risks including:<\/p>\n

                \n
              • Hallucination<\/li>\n
              • Incorrect Outputs<\/li>\n
              • Prompt Injection<\/li>\n
              • Data Privacy<\/li>\n
              • Security<\/li>\n
              • Model Failure<\/li>\n
              • Latency<\/li>\n
              • API Dependency<\/li>\n
              • Cost<\/li>\n
              • Inconsistent Behaviour<\/li>\n<\/ul>\n

                Implement validation, guardrails, fallbacks, monitoring and human review where required.<\/p>\n

                F. AI Evaluation<\/strong><\/p>\n

                  \n
                • Define success criteria before deployment.<\/li>\n
                • Build appropriate test and evaluation processes.<\/li>\n
                • Compare prompts, models and architectures.<\/li>\n
                • Measure quality, accuracy, latency and cost.<\/li>\n
                • Evaluate failures instead of only successful examples.<\/li>\n
                • Use evidence when deciding whether an AI feature is ready to scale.<\/li>\n<\/ul>\n

                  G. AI-Native Development Workflow<\/strong><\/p>\n

                  Use AI effectively across:<\/p>\n

                  Coding \u2022 Debugging \u2022 Testing \u2022 Refactoring \u2022 Documentation \u2022 Research \u2022 Prototyping \u2022 Code Review<\/strong><\/p>\n

                  AI should make you faster, but not careless.<\/p>\n

                  You remain responsible for understanding and validating what is shipped.<\/p>\n

                  H. Explore New AI Opportunities<\/strong><\/p>\n

                    \n
                  • Stay current with rapidly changing AI technology.<\/li>\n
                  • Prototype promising capabilities quickly.<\/li>\n
                  • Separate useful technologies from hype.<\/li>\n
                  • Recommend what ToYm should adopt, test, delay or avoid.<\/li>\n
                  • Convert successful experiments into production systems.<\/li>\n<\/ul>\n
                      \n
                    1. END-TO-END OWNERSHIP<\/strong><\/li>\n<\/ol>\n

                      You may work with:<\/p>\n

                      Product \u2022 Technology \u2022 UI\/UX \u2022 Growth \u2022 Content \u2022 Operations \u2022 Management \u2022 Other Developers<\/strong><\/p>\n

                      You should be able to move through:<\/p>\n

                      Understand Problem \u2192 Determine Whether AI Helps \u2192 Design \u2192 Prototype \u2192 Evaluate \u2192 Build \u2192 Deploy \u2192 Monitor \u2192 Improve<\/strong><\/p>\n

                      Your responsibility is not:<\/p>\n

                      “The model returned an answer.”<\/strong><\/p>\n

                      Your responsibility is:<\/p>\n

                      “The complete system produces a useful and reliable outcome.”<\/strong><\/p>\n

                      \n Job Requirements <\/h3>\n
                        \n
                      1. QKSC – WHO WE ARE LOOKING FOR<\/strong><\/li>\n<\/ol>\n

                        We assess candidates through:<\/p>\n

                        Q – Qualification<\/strong>
                        Do you have evidence of building real AI-powered software?<\/p>\n

                        K – Knowledge<\/strong>
                        Do you understand how modern AI systems actually work?<\/p>\n

                        S – Skill<\/strong>
                        Can you build and deploy them?<\/p>\n

                        C – Competency<\/strong>
                        Can you repeatedly turn uncertain technology into useful results?<\/p>\n

                        Our hiring approach follows:<\/p>\n

                        Ask \u2192 Evidence \u2192 Scenario \u2192 Practical Test \u2192 Reference Check<\/strong><\/p>\n

                          \n
                        1. Q – QUALIFICATION<\/strong><\/li>\n<\/ol>\n

                          There is no strict degree requirement and no fixed minimum number of years<\/strong>.<\/p>\n

                          You must, however, demonstrate real evidence of AI development.<\/p>\n

                          You should have personally built or significantly contributed to at least one:<\/p>\n

                            \n
                          • AI-Powered Application<\/li>\n
                          • LLM Feature<\/li>\n
                          • AI Agent<\/li>\n
                          • RAG System<\/li>\n
                          • AI Automation<\/li>\n
                          • AI-Integrated Software Product<\/li>\n
                          • Other Meaningful AI System<\/li>\n<\/ul>\n

                            Where available, provide GitHub, live systems, demonstrations, architecture examples, portfolio work or other verifiable evidence.<\/p>\n

                            We want to understand:<\/p>\n

                            What did YOU personally build?<\/strong><\/p>\n

                            A working system with clear technical thinking is more valuable than a long list of AI tools on a CV.<\/p>\n

                              \n
                            1. K – KNOWLEDGE<\/strong><\/li>\n<\/ol>\n

                              You should demonstrate practical understanding of areas such as:<\/p>\n

                              LLMs \u2022 Prompt & Context Design \u2022 Structured Outputs \u2022 Tool Calling \u2022 Agents \u2022 RAG \u2022 Embeddings \u2022 Vector Search \u2022 Model Selection \u2022 AI APIs \u2022 Evaluation \u2022 Hallucination Management \u2022 Guardrails \u2022 Security \u2022 Prompt Injection \u2022 Token \/ API Cost \u2022 Latency \u2022 Context Windows \u2022 Data Privacy \u2022 Fallback Strategies \u2022 Monitoring<\/p>\n

                              You should also understand software engineering fundamentals involving:<\/p>\n

                              Backend Development \u2022 APIs \u2022 Databases \u2022 Authentication \u2022 Git \u2022 Testing \u2022 Deployment \u2022 Cloud Infrastructure \u2022 System Integration<\/p>\n

                              You do not need to know every model or framework.<\/p>\n

                              AI technology changes quickly.<\/p>\n

                              We care more about whether you understand the fundamentals and can adapt.<\/p>\n

                                \n
                              1. S – SKILL<\/strong><\/li>\n<\/ol>\n

                                You should be able to:<\/p>\n

                                  \n
                                • Build an AI feature from idea to production.<\/li>\n
                                • Integrate AI models through APIs.<\/li>\n
                                • Build RAG or retrieval workflows where appropriate.<\/li>\n
                                • Implement structured outputs and tool calling.<\/li>\n
                                • Build agentic or automated workflows.<\/li>\n
                                • Connect AI systems to databases and external services.<\/li>\n
                                • Evaluate model output systematically.<\/li>\n
                                • Control reliability, latency and cost.<\/li>\n
                                • Build appropriate fallbacks and guardrails.<\/li>\n
                                • Debug AI and conventional software problems.<\/li>\n
                                • Deploy and monitor AI-powered applications.<\/li>\n
                                • Use AI to improve your own development productivity.<\/li>\n
                                • Explain why you selected a particular model or architecture.<\/li>\n<\/ul>\n
                                    \n
                                  1. C – COMPETENCY<\/strong><\/li>\n<\/ol>\n

                                    We particularly value:<\/p>\n

                                    Ownership<\/strong> – You take responsibility for the complete outcome.<\/p>\n

                                    Problem Solving<\/strong> – You start with the problem, not with an AI tool.<\/p>\n

                                    Experimentation<\/strong> – You test ideas quickly and learn from evidence.<\/p>\n

                                    \u9ad8\u6548\u6267\u884c<\/strong> – You convert prototypes into usable software.<\/p>\n

                                    Product Thinking<\/strong> – You understand whether AI creates genuine user or business value.<\/p>\n

                                    Critical Judgement<\/strong> – You recognise when AI should not be used.<\/p>\n

                                    Learning Agility<\/strong> – You can continuously adapt as models and tools change.<\/p>\n

                                    \u52c7\u62c5\u8d23\u4efb<\/strong> – You consider privacy, security, reliability and cost.<\/p>\n

                                    Communication<\/strong> – You can explain AI concepts clearly to non-AI specialists.<\/p>\n

                                    Working Under Ambiguity<\/strong> – You can explore uncertain problems and progressively determine the right solution.<\/p>\n

                                      \n
                                    1. WHAT MATTERS MORE THAN YEARS OR CERTIFICATES<\/strong><\/li>\n<\/ol>\n

                                      We want to understand:<\/p>\n

                                        \n
                                      • What AI systems have you built?<\/li>\n
                                      • What problem did they solve?<\/li>\n
                                      • What did you personally develop?<\/li>\n
                                      • Which model or architecture did you choose and why?<\/li>\n
                                      • How did you evaluate quality?<\/li>\n
                                      • What happened when the AI produced incorrect results?<\/li>\n
                                      • How did you manage cost and latency?<\/li>\n
                                      • Was it deployed to real users?<\/li>\n
                                      • What measurable result did it create?<\/li>\n
                                      • Can you move beyond a prototype into production?<\/li>\n
                                      • What would you change if usage increased significantly?<\/li>\n<\/ul>\n
                                          \n
                                        1. BITESHORTS & TOYM CONTEXT<\/strong><\/li>\n<\/ol>\n

                                          You may work on AI opportunities across BiteShorts and other ToYm products<\/strong>.<\/p>\n

                                          Potential areas may include:<\/p>\n

                                          Content Discovery \u2022 Search \u2022 Personalisation \u2022 Recommendation \u2022 Content Operations \u2022 Creator Tools \u2022 Merchant Tools \u2022 Advertising \u2022 Customer Support \u2022 Analytics \u2022 Workflow Automation \u2022 Internal Productivity \u2022 New AI-First Products<\/p>\n

                                          We do not want to add AI simply because AI is popular.<\/p>\n

                                          Every implementation should answer:<\/p>\n

                                          What problem are we solving?
                                          Why is AI appropriate?
                                          How do we measure whether it works?<\/strong><\/p>\n

                                            \n
                                          1. SELECTION PROCESS<\/strong><\/li>\n<\/ol>\n

                                            Shortlisted candidates may be asked to:<\/p>\n

                                              \n
                                            • Demonstrate an AI system they personally built.<\/li>\n
                                            • Explain its architecture and model selection.<\/li>\n
                                            • Explain how they evaluated output quality.<\/li>\n
                                            • Diagnose a scenario involving hallucination, latency, cost or reliability.<\/li>\n
                                            • Design an AI solution for a real ToYm or BiteShorts use case.<\/li>\n
                                            • Complete a practical assessment involving AI integration, RAG, agents, APIs, debugging or system design.<\/li>\n<\/ul>\n

                                              We may also ask:<\/p>\n

                                              If the prototype works for 100 users, what changes when it must support 100,000?<\/strong><\/p>\n

                                              We want to understand both your AI capability and engineering judgement<\/strong>.<\/p>\n

                                                \n
                                              1. OUR VALUES<\/strong><\/li>\n<\/ol>\n

                                                CUSTOMER-CENTRIC<\/strong>
                                                PURPOSE-DRIVEN & RESULTS-ORIENTED<\/strong>
                                                TEAMWORK<\/strong>
                                                RESPONSIBILITY<\/strong>
                                                EFFECTIVE EXECUTION<\/strong>
                                                HUMBLE & LEARNING<\/strong>
                                                PURSUIT OF EXCELLENCE<\/strong>
                                                CONTINUOUS INNOVATION<\/strong>
                                                CONTRIBUTE TO SOCIETY<\/strong><\/p>\n

                                                  \n
                                                1. THIS ROLE IS FOR YOU IF…<\/strong><\/li>\n<\/ol>\n

                                                  You already use AI to change how software is built.<\/p>\n

                                                  But you understand that being AI-native means more than prompting a model.<\/p>\n

                                                  You can combine AI capability + software engineering + product thinking<\/strong> to build systems that actually work.<\/p>\n

                                                  You experiment quickly, evaluate objectively and strengthen what succeeds before putting it into production.<\/p>\n

                                                  And you want to help build AI capabilities across BiteShorts and ToYm’s growing digital products.<\/p>\n

                                                  To Apply:<\/strong> Submit your resume and, where available, GitHub, technical portfolio or examples of AI applications, agents, automations or systems you personally built. Be prepared to explain the problem, architecture, model choice, personal contribution, evaluation method and result.<\/p>\n<\/section>\n\t\t\t