Top Artificial Intelligence Jobs in Malaysia

Lead APAC master data and AI-ready metadata product strategy and roadmap. Define golden records, governance, semantic layers, and data contracts. Partner with stakeholders to deliver interoperable, model-ready data products, drive adoption, and ensure compliance, data quality, and scalable analytics across commercial platforms.

Design and deliver enterprise-grade data and AI solutions across the insurance domain. Architect Databricks-based lakehouse platforms, ETL/ELT pipelines, MLOps and Agentic AI solutions; enforce governance (Unity Catalog), responsible AI practices, and scalable cloud deployments (Azure/GCP). Engage senior stakeholders, build business cases, mentor teams, and ensure production-ready, governed data products supporting actuarial, IFRS17, finance, HR, risk, and MI reporting.

Operate in the CEO Office to translate CEO priorities into execution across product, hiring, licensing, partnerships and market launches. Track OKRs, owners, decisions and blockers; prepare briefs, research notes and action trackers; coordinate cross-functional teams; identify and close execution gaps; and own urgent cross-departmental business problems.

Design and implement production-ready AI features and integrations (Azure OpenAI, Alibaba PAI), build RAG pipelines, MCP agents, and data pipelines for extraction, embeddings, and vector storage. Implement prompt engineering, safety guardrails, evaluation, and monitoring for AI-powered SaaS augmentation and partner experiences.

Lead development and deployment of AI-driven smart factory solutions (command & control, decision analytics, image AI, real-time analytics). Build a factory "digital brain" (chatbot, training, control), integrate models into machine control, run big-data analytics, mentor trainees, and ensure 24/7 solution availability while driving investment discussions and external innovation partnerships.

The AI DevOps Engineer will design and maintain cloud infrastructure for AI/ML solutions, implement CI/CD pipelines, and support MLOps lifecycle, ensuring system reliability and performance.

Develop and optimize AI/ML solutions, collaborate with teams, support deployment, build MLOps pipelines, and ensure code quality.

Lead AI and automation product initiatives for Seller Center, partnering with ML, engineering and regional teams to discover, design, validate and launch AI-driven features. Use qualitative and quantitative signals to prioritize work, run model and feature evaluations (A/B tests, shadow/offline tests, cohort analysis), communicate trade-offs to stakeholders, and ensure cross-market alignment and operational scalability.

Work as an AI-focused engineering intern building internal AI tooling and proof-of-concept integrations on Xsolla APIs/SDKs. Ship production-grade code used by internal teams or customers, collaborate with senior engineers and other interns, use agentic coding tools as a default, and present weekly demos of tangible results.

Review, evaluate, and annotate AI-generated text, image, audio, and video in Simplified Chinese. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve annotation guidelines and datasets for generative AI models.

Drive revenue by sourcing and qualifying public and private sector leads, managing enterprise pipeline, preparing proposals and tenders, negotiating and closing deals, coordinating MSA/SOW/PO and basic contracts, aligning partner solutions, and maintaining CRM and reporting.

Lead design and scaling of AI-first operational systems across the company. Improve processes, documentation, reporting, automation, and cross-functional execution while partnering with executive leadership to increase efficiency, KPI visibility, and AI adoption.

As a Product Manager, you will own the end-to-end product delivery, manage AI and non-AI initiatives, ensure stakeholder alignment, and drive successful GTM execution.

Design, develop, and deploy machine learning and Generative AI solutions across the full AI lifecycle. Tasks include data exploration, feature engineering, model training and evaluation, RAG and LLM work, MLOps pipelines, production deployment with software teams, and documenting methodologies to deliver business value.

Lead design, development, testing, and App Store submission of production-scale iOS features in Swift. Collaborate cross-functionally, enforce code quality via reviews and refactoring, maintain technical documentation, and adopt AI-assisted tooling to improve productivity across large, user-facing modules.

Lead AI transformation initiatives, designing and implementing AI solutions to enhance automation and efficiency in e-commerce operations. Drive collaboration across departments, manage pilot projects, and ensure successful adoption of AI technologies.

The AI Consultant will identify automation opportunities, design AI-powered workflows, and collaborate to implement solutions that improve efficiency and accuracy across e-commerce operations.

Design and ship a 14-domain skills taxonomy and SDK/CLI tooling, operate the MCP server, integrate agent protocols, improve LLM grounding for crypto, run developer hackathons, and produce technical content to grow the ecosystem.

Support rollout and adoption of Microsoft Copilot and other GenAI tools across GBS: assist data preparation and prompt engineering, train users, identify use cases, monitor usage, gather feedback, optimize performance, and contribute to documentation and reporting.

Lead design, delivery, and operationalization of production-grade AI systems and agentic workflows. Define architectures, context engineering (prompts, retrieval augmentation, memory), APIs, observability, evaluation frameworks, and quality gates. Drive cross-team technical leadership, mentor engineers, translate business needs into scalable solutions, and establish reusable standards and governance for enterprise AI delivery.

Support software product operations for AI-related software and SPC products: manage release readiness, licensing setup, onboarding, issue reproduction and tracking, documentation, training materials, and collect product feedback across teams.

Lead development and governance of enterprise cybersecurity architecture for cloud, data, and AI. Provide technical leadership, evaluate and select security solutions, oversee risk assessments and governance, mentor architecture teams, support incident response planning, and communicate security strategy to stakeholders to enable secure cloud, data, and AI adoption.

Seeking detail-oriented reviewers for AI training projects, evaluating outputs, generating content, and ensuring cultural relevance. Requires strong analytical skills and attention to detail.

Reviewers will evaluate AI prompts and outputs, correct texts, analyze multimedia, and provide feedback based on cultural relevance and accuracy.