Top Artificial Intelligence Jobs in Malaysia

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Early-career intern will support AI projects for compliance and risk (KYC, KYB, EDD). Tasks include testing and optimizing LLM/Agent workflows, reviewing prompts and model outputs, documenting findings and edge cases, supporting small-scale implementations, and performing operational analysis to improve processes.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.

Build and maintain full-stack web applications using React/Vue frontends and Java/Spring Cloud microservices. Design and consume REST/AI model APIs, work with SQL and NoSQL databases, use AI-assisted development tools, own features end-to-end, collaborate across product and design, ensure high-quality UI/UX, write tests, and contribute to documentation and architecture.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Early-career research role designing and running experiments on LLM reasoning, post-training alignment, and test-time scaling. Implement model variants and training pipelines in PyTorch/Hugging Face, evaluate on crypto-native data, maintain experiment tracking (W&B), and partner with engineering to translate research to production.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics 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 Data & AI Pre-sales Specialist will engage with enterprise clients to provide data and AI solutions, conduct assessments, and guide strategy development. This role also involves pre-sales technical design and market engagement with key tech partners.

Support AI capability and adoption through structured prompt testing, custom GPT configuration, workflow prototyping, documentation of prompt libraries and exemplars, coaching colleagues, and capturing usage patterns to scale responsible AI across the firm.

Support AI Capability & Adoption through structured prompt testing, custom GPT configuration and validation, lightweight workflow prototyping, documentation of prompt libraries and exemplars, coaching/office hours, and tracking usage patterns to scale responsible AI adoption.

Design, develop, and deploy machine learning models, build and optimize data pipelines/ETL, perform exploratory analysis, ensure data quality, collaborate with engineering/product/business stakeholders, and deliver analytics-driven insights to support business objectives.

Rotate across departments to identify and implement AI and automation opportunities, build proofs-of-concept, automate data tasks and dashboards, document improved workflows, and deliver internal training and reports on AI use cases.

Lead a team designing, delivering, and scaling AI and automation solutions across Azure and Microsoft 365. Manage hybrid identity, Azure operations, M365 administration, security/compliance, on-prem servers, vendor relations, and AI strategy (Copilot, OpenAI, Cognitive Services). Oversee governance, POCs, and enterprise adoption while providing L2/3 support and project delivery.

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.

Build and ship AI-native systems for BJAK's Neobank: AI agents, LLM-driven apps, RAG/retrieval systems, automations for onboarding, KYC, support, risk and operations; work with product and engineering to deploy reliable, secure, low-latency solutions and convert manual processes into scalable AI-enabled systems.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

The role involves designing and building AI software solutions, collaborating with teams, and maintaining scalable applications while utilizing Agile methodologies.

Lead AI transformation across Commercial: identify, prioritize, and deliver AI initiatives (forecasting, pricing, personalization, copilots, automation) that drive revenue, productivity, and data-driven decisions; translate business problems into scalable AI solutions; partner with Technology, Legal, Risk, and Data teams; build AI literacy and lead an execution-focused team.

Lead adoption and implementation of AI and automation across Commercial teams. Identify opportunities, gather requirements, coordinate testing, improve reporting and dashboards, enable AI literacy, and ensure governance, data privacy, and responsible AI practices to enhance operational efficiency and commercial decision-making.