Top Machine Learning Jobs in Kuala Lumpur

Own product initiative delivery from kickoff through release, coordinating Product, Engineering, Data, and Solutions. Track dependencies, risks, blockers, scope changes, timelines, testing, handoffs, and launch readiness. Maintain accurate project status and documentation while ensuring execution remains aligned with product intent. This hands-on role requires technical fluency, cross-functional communication, strong ownership, and daily overlap with an Asia-based technology team.

Leads enterprise presales and consultative selling for Experian’s Ascend Platform across Malaysia. Develops the presales practice, originates and structures multimillion-dollar SaaS deals, engages C-suite banking and lending stakeholders, and architects end-to-end credit risk and decisioning solutions. Responsibilities include technical demonstrations, workshops, proposals, architecture blueprints, RFP responses, business process assessments, outbound campaigns, and collaboration on data science, GenAI, ModelOps, and real-time decisioning solutions.

Analyze and interpret complex data using machine learning, artificial intelligence, and advanced analytics. Transform findings into actionable insights that support organizational decision-making, business strategy, and improved outcomes. Clearly communicate technical concepts and data insights to both technical and non-technical audiences.

Support APAC Claims Operations by producing and automating reports, reconciling multi-system data, resolving quality issues, and developing scalable dashboards. Build SQL queries, Python automation workflows, data validation processes, and documentation. Deliver ad hoc analysis and leadership insights while piloting AI applications such as claims classification, anomaly detection, document summarization, and natural-language querying. Partner across markets to improve reporting efficiency, data quality, and operational decision-making.

Leads APAC Data and AI enablement by aligning regional business priorities with enterprise technology strategy. Translates business problems into scalable use cases, roadmaps, and measurable outcomes; partners with platform, data science, engineering, risk, compliance, and operations teams; drives adoption and value realization; and builds responsible AI capabilities through training, governance, champion networks, and reusable practices.

Leads AI- and data-driven manufacturing transformation strategies for Life Sciences clients. Advises C-suite and operations leaders, develops AI strategies, business cases, roadmaps, operating models, and responsible AI plans, and oversees large-scale greenfield and brownfield manufacturing programs. Drives digital twin, automation, data platform, advanced analytics, generative AI, and AI/ML deployments from proof of concept through enterprise scale. Builds executive relationships, originates opportunities, manages partner ecosystems, develops manufacturing capabilities, and leads teams, delivery, financial performance, and risk.

Leads enterprise-wide business analysis and digital transformation initiatives across Operations. Responsibilities include gathering requirements, mapping processes, developing business cases, redesigning customer experiences, identifying AI and automation opportunities, managing transformation roadmaps, influencing senior stakeholders, supporting solution delivery, and tracking governance, performance, risks, and benefits realization.

The Sales Manager will drive customer acquisitions and revenue growth, manage complex sales cycles, and present Cloud solutions at executive levels while maintaining long-term relationships with clients.

The Junior Wind Engineer will analyze wind loading on various structures, solve engineering problems, and support project delivery and business development.

The AI/ML Engineer develops and integrates AI models into production systems, optimizing performance and supporting project teams with leadership and mentorship.

The Senior Gen AI Engineer leads the design and strategy for Generative AI use cases, focusing on user research, product management, and collaboration with cross-functional teams to drive AI innovations and solutions.

This role involves leading AI use case discovery and strategy, user research, product concepting, and prioritization of AI initiatives, ensuring alignment with customer needs and business value.

Perform remote part-time data tasks to improve Malay AI content, including data collection, evaluation, annotation, pairwise comparisons, counting, and object tagging across audio, video, images, and collected data.

Supports data science and analytics solution development that drives business growth, customer experience, productivity, and efficiency. Responsibilities include delivering analytics workplans, developing statistical, predictive, segmentation, and propensity models, creating reusable best practices, supporting business decisions, and collaborating with multidisciplinary teams. The role requires programming-based analytics expertise, machine learning knowledge, strong analytical and problem-solving skills, business acumen, and project management capabilities.

As an AI Data Specialist, you will support AI-generated content improvement in Chinese Simplified through tasks like data collection, evaluation, and annotation.

Provides technical consulting for AML, sanctions screening, and third-party risk solutions across APAC. Leads RFPs, sales engagements, client implementations, API integrations, data pipelines, analytics, reporting, and risk decisioning initiatives. Advises customers on data architecture, modeling, transformation, and enrichment while serving as a technical SME and customer advocate. Partners with sales, product, and delivery teams, presents at industry events, and monitors regulatory, technology, and competitive developments.

Leads finance transformation by owning core financial processes, modernizing ERP and planning systems, deploying AI and automation, partnering with engineering and data teams, redesigning workflows, establishing controls, and driving organizational adoption. The role requires senior finance leadership, strong systems expertise, hands-on AI transformation experience, process governance, and the ability to translate between finance and technology.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.

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 product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

The Data Annotator is responsible for labeling datasets, reviewing annotations for quality, collaborating with AI teams, and meeting annotation targets.