Top Artificial Intelligence Jobs in Malaysia

Support AI/ML initiatives by developing, training, and evaluating models; preprocessing and analyzing data; building prototypes; conducting literature reviews; documenting workflows; automating tasks with Python and APIs; and collaborating with cross-functional teams.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The role involves delivering AI solutions, driving innovation with emerging techniques, shaping AI architecture, and empowering the data community within IRAS.

The Manager/Senior Manager for AI Accelerator and Governance will lead AI adoption, manage AI solutions, ensure governance, and enhance workforce capabilities at IRAS.

Design, deploy and operate cloud-native (AWS) services and APIs for payments. Build CI/CD, IaC, RESTful/OpenAPI services, microservices and containers, troubleshoot distributed systems, optimize performance and integrate AI tooling into engineering workflows.

Record short video and/or audio clips and transcribe/listen to media to provide expressive, emotionally varied voice data for AI training; tasks are remote, project-based, and assigned based on fit and availability.

Record short video and audio clips with expressive vocal performances; transcribe and label audio/video content. Flexible, remote, project-based work; assignments matched by fit and availability.

Design enterprise architecture for workplace GenAI solutions, map ChatGPT Enterprise capabilities to validated use cases, define onboarding and governance standards, establish identity and access controls (SSO/SCIM/RBAC), partner with Risk/Legal/Compliance, assess cost versus productivity, implement security and audit-ready controls, and develop reference architectures and lifecycle management for governed GPT solutions.

Design, develop, test and deploy RPA and agentic AI workflows. Analyze business processes, gather requirements, create BRD/PDD/SDD and agent design artifacts, integrate APIs and services, perform end-to-end and AI output testing, provide L1-L3 support, monitor agent performance, and deliver client engagements onsite and remotely.

Manage AI/automation projects from planning through deployment: lead cross-functional teams, apply AI algorithms (classification, detection, time-series, anomaly detection, reinforcement learning, clustering, recommendation), oversee system integration and ML production deployments, handle risk/resource planning, maintain technical documentation, and use Agile processes and tools (Jira/Confluence).

Design, build, and automate ETL/data pipelines to support AI/ML workflows; integrate backend systems and APIs; ensure data governance, security, and compliance; deploy ML models to production; use SQL, Python, Spark, Hadoop, Kafka and cloud data services.

Design and develop Generative AI applications, building scalable full-stack solutions, managing various libraries, and collaborating across teams to enhance AI user experiences.

The role involves creating SEO, CRO, and GEO-optimized content for the Australian market, leveraging AI tools and ensuring high editorial quality for improved organic traffic and conversions.

The Automation Analyst will design and implement AI-driven automation solutions, leverage GenAI and LLMs, and optimize business processes while collaborating with stakeholders.

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.

Support data pipelines and ML workflows for manufacturing: apply computer vision and ML to detect defects, build and deploy models (APIs/edge), design GenAI/LLM applications, and collaborate with stakeholders to improve yield and quality.

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.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.

Design, build, and productionize multi-agent, agentic AI systems to autonomously validate FPGA configuration and security firmware. Integrate agents with enterprise tools, implement RAG/vector DB pipelines, harden models with validation and cost controls, establish CI/CD and testing for LLM-driven workflows, and mentor engineers.

The AI Infrastructure Engineer designs and implements AI/ML solutions for infrastructure, focusing on automation, security, and cloud management across GCP and multi-cloud environments.

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.

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.

The Director of AI & Chatbot is responsible for transforming OKX's conversational AI capabilities, improving automation and customer experience while leading a technical team and partnering with various departments.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.