Top Machine Learning Jobs in Kuala Lumpur

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

The role leads data and AI strategy for BSC APAC, overseeing data governance and AI solutions to drive business value and digital transformation.

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.

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

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

The Fraud Ops Sr Analyst manages fraud losses, develops policies, and analyzes risks associated with digital banking. Responsibilities include fraud detection, data analysis, collaboration with tech teams, and leadership development.

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.

Design, build, and deploy AI/ML and GenAI solutions (LLMs and AI agents) for IT automation and AIOps. Lead solution ownership in Agile, develop/run machine learning models, integrate open-source and proprietary LLMs, build runbook executors (PowerShell/Python), work with cloud and container platforms, engage stakeholders, and document automation and models.

Maintain and monitor ML models in production, automate and scale data pipelines and model serving, troubleshoot issues, collaborate with cross-functional teams, and promote MLOps best practices and standards.

Manage and operate the Databricks Lakehouse and MLOps platform, ensuring availability, security, governance and cost optimisation. Lead incident/change/service management, define MLOps frameworks and CI/CD for model lifecycle, collaborate with engineering and data science to deploy and monitor models, and manage vendors and platform roadmap.

Design, build, deploy and operate end-to-end ML pipelines on Databricks. Develop production-grade Python and Spark code, implement MLOps practices (MLflow, CI/CD, versioning, monitoring), ensure reproducibility, security and governance, monitor model/data performance, and collaborate with data science, engineering and product teams to deliver scalable, compliant ML services.

Work with Product, Business, and Engineering to collect, clean, analyze data, build Python and SQL automation, contribute to internal analytics platforms, assist cloud deployments, and document technical solutions to support decision-making.

Design, develop, evaluate, and productionize ML models and agentic solutions on Databricks and Microsoft Fabric. Build scalable ETL pipelines, perform feature engineering, run disciplined experiments, monitor models in production, and communicate statistical insights to stakeholders while supporting platform engineering and automation.

Build scalable, cloud-native backend services, large-scale data pipelines, and production ML model deployment on AWS. Own data storage (relational and vector/search), CI/CD, monitoring, and collaborate with ML and frontend engineers to deliver AI-assisted coding and agentic features.

Design and operate cloud-native backend services, large-scale data pipelines, and production ML model deployment. Build APIs, vector/search data storage, CI/CD, observability, and collaborate with ML and frontend engineers to deliver AI-assisted coding and agentic solutions for game development.

The Power ML Engineer will tailor existing models for the Japanese power market, integrate market signals, develop features, monitor performance, and work collaboratively on product roadmaps.