Top Artificial Intelligence Jobs in Malaysia

Partners with senior leaders and business managers on employee relations, workforce planning, talent and succession management, organizational design, compensation, performance, engagement, and career development. Uses people analytics and digital HR tools to guide workforce decisions, improve HR service delivery, and support transformation. Oversees senior employee lifecycle events, including onboarding, mobility, retirement, long absence, redundancies, and performance improvement plans.

Lead the design and delivery of enterprise-scale AI solutions in production. Responsibilities include architecting secure Azure-native AI platforms, developing machine learning, generative AI, LLM, RAG, and agentic AI applications, and implementing MLOps/LLMOps lifecycle management. The role requires technical leadership across engineering and business teams, model evaluation and optimization, monitoring, governance, API integration, deployment pipelines, and applications supporting industrial or chemical processes.

Build and deploy AI enablement tools across GitHub, Jira, Confluence, observability, service management, DevOps, and testing platforms. Develop agentic workflows, document-processing pipelines, retrieval architectures, evaluation and hallucination safeguards. Establish governance controls for access, auditing, data handling, and regulatory compliance. Track adoption, reliability, cost, and business impact while training teams and partnering with architecture, infrastructure, governance, and business stakeholders.

Leads financial crime investigations and the STR/SAR reporting process, ensuring accurate, timely, and defensible regulatory filings. Reviews investigations, narratives, transaction data, and on-chain analysis; establishes quality standards and escalation thresholds; monitors performance; coaches investigators; and collaborates with compliance teams, MLROs, auditors, and regulators on complex cases and emerging risks.

Drive enterprise sales of Microsoft AI workforce solutions, including Microsoft 365, Copilot, and AI agents. Engage senior business and technology leaders, identify transformation opportunities, build business cases, manage complex sales cycles, develop pipeline, forecast accounts, and orchestrate Microsoft teams, services, customer success groups, and partners to accelerate adoption and measurable business outcomes.

Designs and assures enterprise AI solutions from opportunity intake through delivery handoff. Responsibilities include architecture across models, data, applications, infrastructure, integration, and security; feasibility prototyping; commercial and scope development; risk and governance reviews; resource and delivery planning; statements of work; and transition to delivery. The role also develops reusable AI patterns, coaches teams, and engages clients and stakeholders in English and Japanese.

Resolves moderately complex technical customer issues across multiple Zebra products, manages support tickets within SLA targets, collaborates with cross-functional teams, improves support processes, develops training for entry-level staff, and contributes to technical reviews and product improvement insights.

Perform legal data collection, evaluation, and annotation for AI-powered contract management platforms in Simplified Chinese. Analyze clauses, obligations, and terms in international business contracts; participate in onboarding, calibration, and quality reviews; and collaborate within secure client environments. Requires contract or legal expertise, strong legal interpretation and contract-review skills, and fluency in English and Simplified Chinese. Prior data annotation or machine learning evaluation experience is preferred.

Leads responsible deployment of Salesforce Agentforce and Einstein capabilities by defining AI governance policies, risk assessments, guardrails, audit practices, monitoring metrics, and remediation processes. Advises teams on agent design, grounding, prompts, human handoff, privacy, security, compliance, and safe automation. Partners with data, security, legal, architecture, and business stakeholders to enable governed pilots, production readiness, enterprise adoption, and continuous improvement.

Own marketing data quality and governance across CRM and automation tools. Support marketing tech stack, workflows, and campaign infrastructure. Build automated reports and dashboards, document processes, align marketing and sales, and explore AI tools to improve operations and reporting.

Lead data and AI strategy initiatives, develop client solutions, translate business needs into actionable roadmaps, and drive measurable business transformation outcomes.

Produces AI-assisted video, social media, motion graphics, and design content across platforms and regional markets. Responsibilities include scripting, storyboarding, prompting, editing, repurposing assets, channel planning, content optimization, and developing scalable AI workflows. The role evaluates creative tools, trains colleagues, maintains brand consistency, and collaborates with Compliance and Legal to meet financial-services advertising requirements, responsible-AI standards, and regional regulations.

Drives automated test equipment hardware solutions for AI-focused semiconductor devices and modules. Manages load boards, probe cards, sockets, and change kits from sourcing through production release; coordinates vendors, budgets, inventory, forecasts, and cross-functional delivery. Provides technical direction for module testing in production environments, supports NPI timelines and capacity, and identifies margin improvement opportunities.

Build and operate AWS-native AI platforms and production machine learning services for insurance operations. Responsibilities include solution architecture, Terraform infrastructure, CI/CD, SageMaker MLOps, generative AI, RAG, computer vision, serverless pipelines, model monitoring, governance, observability, and enterprise integrations. Senior engineers mentor teammates and lead design reviews; junior engineers implement well-scoped features with guidance.

Manage SAP master data, data governance, lifecycle processes, quality controls, documentation, workflows, audits, and process improvements. Serve as a subject-matter contact for business units, monitor KPIs, validate records, support integrations and testing, and drive automation. Contribute to Evonik’s AI-ready data initiative by standardizing and enriching data, defining metadata and business glossaries, assessing AI use cases, resolving data quality issues, and maintaining discoverable, governed data assets.

Design, implement, and deploy stateful agentic LLM orchestration and RAG systems integrated with internal APIs, databases, and third-party tools. Collaborate in Agile teams to deliver reliable AI-automation solutions, contribute to code review and QA, improve system reliability, and communicate technical trade-offs with stakeholders.

Build and maintain production full-stack applications using Python, React, TypeScript, SQL, APIs, and AWS. Design agentic AI capabilities including LLM orchestration, tool use, retrieval, structured outputs, and workflows. Develop LLM evaluation processes, contribute to system architecture, deploy and operate cloud applications, and improve reliability, security, observability, and performance. Collaborate with consultants and stakeholders to translate market research and consulting workflows into practical AI automation solutions.

Participate in AI initiatives and embedded software development for mission-critical products. Collaborate with software engineering teams, gain exposure to software development lifecycles and agile methodologies, and use C++ and Python in an embedded Linux environment. The internship requires pursuing a Bachelor's degree in Computer Engineering or Computer Science, a minimum 12-week commitment, and availability to work in Bayan Lepas, Penang.

Build scalable backend AI services for financial applications, including model gateways, agent orchestration, tool-calling systems, streaming inference, memory and knowledge storage, observability, and LLM evaluation. Integrate AI with trading, risk, compliance, and customer-service systems while optimizing performance, reliability, security, and output quality. Requires strong Golang or Java backend expertise, microservices experience, and production LLM or AI agent development.

Designs and leads secure architectures for cloud, data, and AI environments. Translates cybersecurity strategies into implementable solutions, evaluates security products, performs risk assessments, ensures regulatory and architecture compliance, and supports engineering teams with secure solution implementation. The role also contributes to incident response planning, resilience, and stakeholder communication across technical and business audiences.

Develops dashboards, KPI monitoring, and analytical reports to identify business trends, risks, root causes, and improvement opportunities. Translates business questions into metrics and analytical requirements, supports predictive and prescriptive AI use cases, validates data quality, documents governance standards, and collaborates with business, IT, and data engineering stakeholders to deliver trusted analytics and AI-enabled solutions.

Serves as the physician-level clinical authority for Elsevier Clinical Solutions across APAC. Supports sales engagements, customer presentations, demonstrations, workshops, solution positioning, post-sale adoption, and thought leadership. Advises healthcare organizations on clinical decision support, AI-enabled solutions, workflow integration, evidence-based practice, patient safety, and clinical quality. Translates clinical needs into solution recommendations, supports commercial growth, gathers customer insights, informs product strategy, and promotes responsible AI adoption in healthcare.

Designs, develops, deploys, and maintains AI, machine learning, generative AI, and agentic AI solutions for manufacturing. The role analyzes complex factory data, builds and optimizes models, automates processes, develops data pipelines, and improves operational decision-making. Responsibilities include collaborating with engineering, manufacturing, quality, data architecture, and analytics teams; deploying and monitoring solutions; identifying business use cases; and delivering scalable data and AI systems.

Analyzes campaign performance across social and digital channels, produces actionable reports, evaluates customer journeys, optimizes funnels, and runs A/B and multivariate tests. The role also identifies growth opportunities, monitors ad-tech trends, supports campaign setup and tracking, and communicates insights to internal teams. Candidates should be analytical, self-directed, detail-oriented, and experienced with Excel, visualization, campaign metrics, Google Analytics, and digital advertising platforms.

Oversee portfolios of Generative AI data operations projects, managing delivery, capacity planning, vendors, budgets, quality, risks, automation, and client reporting. Coach project managers and coordinators, collaborate with recruiting and finance, enforce data security and compliance, and improve processes across distributed teams.