AI Software Engineer (AI-Native Development)

Thousands of Years Media; Malaysia; Hybrid; MYR 120,000 - 180,000; Full time

Job summary

Thousands of Years Media Sdn. Bhd. (ToYm) is seeking an AI Software Engineer (AI-Native Development) to build AI-powered applications and embed AI into core products and workflows across BiteShorts and ToYm platforms.

You will own AI features end-to-end, evaluate models, ensure reliability, security and guardrails, and contribute to AI-native development workflows to accelerate delivery and create measurable business value.

Qualifications

• Evidence of building AI-powered software at scale.
• Ability to integrate AI models and APIs into production systems.
• Experience with AI-enabled workflows and automated processes.
• Understanding of reliability, security and guardrails for AI systems.

Responsibilities

• Build AI-powered products where AI is a core capability.
• Integrate AI models and APIs into production software and turn prototypes into maintainable systems.
• Design AI agents, automation, and end-to-end AI-native development workflows.
• Ensure guardrails, validation, monitoring and security considerations are in place.
• Evaluate model performance, latency and cost to improve business value.

Skills

AI software development

AI model integration

RAG workflows

Tools

AI APIs

RAG frameworks

Job description

AI Software Engineer (AI-Native Development)

AI Software Engineer (AI-Native Development)

Role Overview

AI & Product Technology

Build With AI. Build AI Into Products. Turn New Technology Into Real Business Capability.

Company: Thousands of Years Media Sdn. Bhd. (ToYm)

• ROLE MISSION

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

You may work across BiteShorts, other ToYm applications, internal systems and new AI-driven products.

Your mission is not simply to use AI tools.

Your mission is to:

AI-powered systems that create measurable value.

This may include:

We need someone who understands both AI and software engineering.

A successful prototype is only the beginning.

It must eventually become a system that is reliable, secure, maintainable and useful.

• WHY THIS OPPORTUNITY IS DIFFERENT

AI is changing both:

What software can do

and

How software is built

At ToYm, you will work on both.

You will use AI-native development methods to increase engineering speed while also embedding AI into real products and business workflows.

You may work across entertainment, digital platforms, commerce, content, creator ecosystems and internal operations.

We want someone who can move from:

—not someone who only experiments with prompts or creates demonstrations.

Job Description

• WHAT YOU WILL OWN

A. Build AI-Powered Products

• Build applications where AI is a core capability.
• Integrate AI models and APIs into production software.
• Turn prototypes into maintainable systems.
• Evaluate whether AI is actually the right solution before implementing it.

Work with capabilities such as:

• Structured Outputs
• Tool / Function Calling
• Embeddings
• Semantic Search
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Image, Video, Audio or Text AI services where required

Choose solutions based on quality, reliability, latency, cost and business value.

C. AI Agents & Automation

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

D. Build Complete Software Around AI

AI alone is not a complete product.

You should be capable of building or contributing to:

Build complete systems where AI works as part of a reliable software architecture.

E. Reliability, Security & Guardrails

Design for risks including:

• Hallucination
• Data Privacy
• Security
• Cost
• Inconsistent Behaviour

Implement validation, guardrails, fallbacks, monitoring and human review where required.

F. AI Evaluation

• Build appropriate test and evaluation processes.
• Compare prompts, models and architectures.
• Measure quality, accuracy, latency and cost.
• Evaluate failures instead of only successful examples.
• Use evidence when deciding whether an AI feature is ready to scale.

G. AI-Native Development Workflow

Use AI effectively across:

AI should make you faster, but not careless.

You remain responsible for understanding and validating what is shipped.

H. Explore New AI Opportunities

• Stay current with rapidly changing AI technology.
• Separate useful technologies from hype.
• Recommend what ToYm should adopt, test, delay or avoid.
• Convert successful experiments into production systems.
• END-TO-END OWNERSHIP

You may work with:

You should be able to move through:

Your responsibility is not:

“The model returned an answer.”

Your responsibility is:

“The complete system produces a useful and reliable outcome.”

Job Requirements

• QKSC — WHO WE ARE LOOKING FOR

We assess candidates through:

Q — Qualification
Do you have evidence of building real AI-powered software?

K — Knowledge
Do you understand how modern AI systems actually work?

S — Skill
Can you build and deploy them?

C — Competency
Can you repeatedly turn uncertain technology into useful results?

Our hiring approach follows:

• Q — QUALIFICATION

There is no strict degree requirement and no fixed minimum number of years.

You must, however, demonstrate real evidence of AI development.

You should have personally built or significantly contributed to at least one:

• AI-Powered Application
• LLM Feature
• AI Agent
• RAG System
• AI Automation
• AI-Integrated Software Product
• Other Meaningful AI System

Where available, provide GitHub, live systems, demonstrations, architecture examples, portfolio work or other verifiable evidence.

We want to understand:

What did YOU personally build?

A working system with clear technical thinking is more valuable than a long list of AI tools on a CV.

• K — KNOWLEDGE

You should demonstrate practical understanding of areas such as:

You should also understand software engineering fundamentals involving:

You do not need to know every model or framework.

We care more about whether you understand the fundamentals and can adapt.

• S — SKILL

You should be able to:

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

We particularly value:

Ownership — You take responsibility for the complete outcome.

Problem Solving — You start with the problem, not with an AI tool.

Experimentation — You test ideas quickly and learn from evidence.

Effective Execution — You convert prototypes into usable software.

Product Thinking — You understand whether AI creates genuine user or business value.

Critical Judgement — You recognise when AI should not be used.

Learning Agility — You can continuously adapt as models and tools change.

Responsibility — You consider privacy, security, reliability and cost.

Communication — You can explain AI concepts clearly to non-AI specialists.

Working Under Ambiguity — You can explore uncertain problems and progressively determine the right solution.

• WHAT MATTERS MORE THAN YEARS OR CERTIFICATES

We want to understand:

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

You may work on AI opportunities across BiteShorts and other ToYm products.

Potential areas may include:

We do not want to add AI simply because AI is popular.

Every implementation should answer:

What problem are we solving?
Why is AI appropriate?
How do we measure whether it works?

• SELECTION PROCESS

Shortlisted candidates may be asked to:

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

We may also ask:

If the prototype works for 100 users, what changes when it must support 100,000?

We want to understand both your AI capability and engineering judgement.

• OUR VALUES

CUSTOMER-CENTRIC
PURPOSE-DRIVEN & RESULTS-ORIENTED
TEAMWORK
RESPONSIBILITY
EFFECTIVE EXECUTION
HUMBLE & LEARNING
PURSUIT OF EXCELLENCE
CONTINUOUS INNOVATION
CONTRIBUTE TO SOCIETY

You already use AI to change how software is built.

But you understand that being AI-native means more than prompting a model.

You can combine AI capability + software engineering + product thinking to build systems that actually work.

You experiment quickly, evaluate objectively and strengthen what succeeds before putting it into production.

And you want to help build AI capabilities across BiteShorts and ToYm’s growing digital products.