Top AI & Machine Learning Jobs in Singapore

Lead design and delivery of generative AI agents and architectures, build robust APIs and scalable systems, roadmap AI applications, collaborate cross-functionally, troubleshoot production issues, and drive internal efficiency and customer-facing AI solutions.

Lead technical strategy for payments optimization to balance conversion, fraud risk, and cost. Design and implement advanced optimization algorithms, guide experimentation and causal inference, collaborate with product and engineering to deploy ML systems at scale, influence product strategy, and mentor junior data scientists.

Lead architecture of a governed, reusable data, knowledge, and skills platform on Databricks to power analytics, AI agents, and real-time decisioning. Design data, semantic, and skills layers, enforce Governance-as-Code, enable regional data residency, and build agent interfaces and APIs while partnering with Data Science, Product, and Risk teams.

Lead end-to-end design and implementation of generative AI solutions and APIs to improve customer-facing and internal automation. Build scalable, high-concurrency backend systems, collaborate with cross-functional teams, set best practices, and mentor engineers while driving deployment and MLOps for production AI products.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform solutions, build scalable multi-region pipelines, enforce governance and security, mentor the team, advocate best practices, and influence technical roadmaps to enable AI/ML-driven business impact.

Lead the Realtime Data Platform team to define a 1–3 year technical roadmap, modernize the core platform for petabyte-scale real-time analytics, partner with product teams to enable data-driven features, scale and structure engineering teams, and serve as a trusted technical advisor across Data and AI initiatives.

Partner with Product and Engineering to analyze large datasets, run experiments, and apply statistical methods to measure product performance, identify growth opportunities, set goals and forecasts, and drive product strategy through data-driven recommendations.

Partner with Product and Engineering to analyze large datasets, run experiments, build statistical models, define product metrics, forecast outcomes, and deliver insights that drive product strategy and roadmap decisions.

Lead design and build of high-performance distributed data infrastructure and AI enablement platforms. Optimize systems internals and JVM performance, implement lakehouse and real-time streaming, develop scalable vector indexing and AI knowledge platform, own SDLC for petabyte-scale systems across global regions, drive engineering best practices, and mentor mid-level engineers.