Top AI Jobs in Singapore

Owns APJ revenue for Cohesity’s GAIA AI platform, including product, infrastructure, consulting, and advisory bookings. Builds pipeline, develops enterprise AI opportunities, leads executive presentations, closes complex deals, and manages forecasts. Partners with field sales, marketing, SDRs, NVIDIA, hyperscalers, infrastructure OEMs, and AI vendors in a specialist co-sell model. Requires strong AI solution knowledge, enterprise quota-carrying sales experience, and credibility with C-level stakeholders.

Lead end-to-end delivery of innovation and emerging-technology programs in a matrixed bank environment. Align stakeholders, enforce governance, manage scope, risks, testing, and vendor integrations to deliver compliant, measurable outcomes.

Lead product direction, roadmaps, standards, metrics, execution, and quality for major areas of KIRA’s AI neobank app. Guide PMs, product owners, designers, and engineers across financial services including insurance, payments, savings, investing, and travel. Translate regulatory and business constraints into product decisions, drive launches and continuous improvement, and foster a fast, user-focused product culture.

Conduct financial crime investigations using AI, network-link analysis, OSINT, transaction reviews, and customer relationship data. Identify money laundering, fraud, sanctions evasion, corruption, mule activity, emerging threats, and criminal networks. Develop investigative methodologies and intelligence products, prepare reports and presentations, and collaborate with compliance, business, and control-function stakeholders. Apply AML, CTF, sanctions, and financial crime risk expertise within banking and wealth management.

Own AI Investing product areas across financial services, defining user problems, product direction, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Launch features, monitor performance, use data and feedback to guide iterations, and balance speed, user experience, business impact, compliance, and operational complexity.

Own the end-to-end product direction and quality of an AI-native email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI workflows.

Own the end-to-end definition, evaluation, and quality of AI-powered email experiences. Translate model capabilities and technical constraints into product requirements, system decisions, priorities, and measurable outcomes. Partner with ML, backend, and client engineers to improve reliability, safety, latency, cost, usability, and trust. Identify failure modes, establish quality bars, drive experimentation, and guide execution in a fast-moving zero-to-one environment.

Own the strategy, requirements, quality, and execution of an AI-native email application. Partner closely with ML, backend, and client engineers to define model and system behavior, evaluation frameworks, product trade-offs, and reliable user experiences. Drive prioritization, experimentation, failure-mode mitigation, and continuous iteration while balancing quality, latency, cost, safety, reliability, and user trust in a fast-moving zero-to-one environment.

Owns the end-to-end product strategy and execution for AI-powered systems, translating model capabilities, technical constraints, and user needs into measurable requirements and product decisions. Partners closely with ML, backend, and client engineering teams on system design, evaluation, experimentation, reliability, safety, and user experience. Establishes quality standards, identifies failure modes, prioritizes roadmaps, and drives rapid iteration for predictable, useful, and trusted AI products.

Develop and validate AI-driven algorithms for advanced optical metrology and semiconductor analytics. Responsibilities include researching methodologies, analyzing technical data, prototyping and fine-tuning machine learning models, benchmarking performance, defining product features, writing software requirements, and collaborating with application, product, marketing, and software engineering teams to deploy reliable production solutions.

Lead technical AI security strategy for enterprise customers, advising on LLMs, RAG, agents, and data-protection architectures. Drive technical sales from discovery to close, deliver tailored demos and POVs, inject product feedback, and build reusable enablement assets and training to scale field adoption of secure AI solutions.

Lead AI-enabled cost transformation across procurement and manufacturing. Own product backlog and roadmap, define requirements with data scientists and engineers, prioritize AI/Generative AI cost use cases, drive delivery, measure before/after savings, and embed workflow optimizations into daily operations.

Design, build, and operate AI agents that automate SRE, DevOps, and DBA workflows across global infrastructure. Develop safe agent architectures with planning, reasoning, tool execution, error recovery, evaluation, and human escalation. Integrate agents with Kubernetes, Terraform, cloud APIs, databases, monitoring platforms, and CI/CD systems. Collaborate with infrastructure teams to identify automation opportunities and shape Airwallex’s infrastructure AI strategy.

Design, build, evaluate, deploy, and monitor LLM-powered agentic systems for regulatory workflows. Develop LangGraph/LangChain agents, evaluation and observability pipelines, RAG and vector-search systems, and production LLMOps practices. Integrate AI services into Node and React applications while optimizing prompts for cost, latency, and accuracy. Collaborate with product, engineering, and regulatory stakeholders, and mentor colleagues on agent design and evaluation.

Lead the design and production delivery of enterprise AI solutions, combining hands-on AI engineering with technical architecture and multidisciplinary team leadership. The role covers LLMs, agentic tooling, cloud-native AI platforms, software architecture, data engineering, DevOps, responsible AI, and stakeholder collaboration across enterprise and public-sector environments.

Support ML engineers operating production training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through automation, tooling, documentation, runbooks, and operational processes. The role is remote in the Philippines or Singapore and follows a Sunday-Wednesday APAC shift.

Drives enterprise adoption and business value for Cursor by building relationships with engineering leaders, guiding rollout and AI usage best practices, coordinating with Sales and Field Engineering, influencing the product roadmap, and managing account health, renewals, and expansion. The role requires strategic account planning, executive communication, technical understanding of software development workflows, and customer-facing experience in enterprise software.

Leads AI engineering for APAC Global Broking, partnering with brokers and business leaders to identify, design, and deliver productivity solutions. Develops AI agents, workflow automations, retrieval systems, document tools, and integrations from prototype through production. Ensures governance, security, architecture, testing, risk, adoption, and measurable benefits while coordinating business, technology, and control stakeholders.

Supports Mastercard’s Agent Pay and AI innovation programs across Greater China by coordinating pilots, proofs of concept, prototypes, deployments, client engagements, ecosystem partnerships, and market adoption. Manages project plans, stakeholders, risks, milestones, governance, documentation, and performance metrics. Collaborates with product, engineering, innovation, sales, clients, fintechs, banks, and technology partners to develop AI-powered payment and commerce solutions while considering security, privacy, compliance, responsible AI, and risk.

Build and maintain LLM-powered, agent-based tools that automate developer and quantitative researcher workflows. Collaborate with platform and infrastructure teams to deliver scalable solutions, applying prompt engineering, tool integration, access controls, and observability. Own projects from prototyping through deployment, rollout, and documentation while promoting strong engineering standards. The role requires Python, LLM API integration, AI application development, Linux, networking, and software engineering fundamentals.

Lead adoption and scaling of AI across supply chain: define roadmap, prioritize use cases, manage solution lifecycle from pilots to production, establish AI governance and playbooks, improve data readiness, drive stakeholder engagement, build community of practice, and measure business value.

Own full sales cycle for venture-backed startups in Singapore: source prospects, close deals, onboard customers, and grow accounts. Translate Airwallexs AI finance (AgentOS) into concrete use cases, run demos/discovery, build VC partnerships, seed customer stories, and represent the company at community events.

Lead strategic transformation of group payment operations through platform modernization, workflow automation, process re-engineering, and data-driven improvements. Define the target operating model and partner with Technology, Product, Risk, Compliance, and Operations teams to deliver scalable payment solutions. Own business requirements, solution reviews, testing, implementation, governance, operational readiness, and stabilization. Drive modernization across SWIFT, cross-border, real-time, domestic, and ISO 20022 payment environments while improving efficiency, resilience, controls, scalability, and client experience.

Lead AI-enabled cost transformation across procurement and manufacturing, build and operationalize a portfolio of AI/Generative AI use cases (spend, supplier, contract analytics), implement measurable savings and productivity improvements, embed solutions into workflows, manage cross-functional stakeholders, and scale data science and automation initiatives for value capture.

Lead the design, prototyping, development, deployment, and integration of generative AI solutions, including LLMs and GANs. Provide hands-on technical leadership, guide software development practices, mentor engineers, collaborate with product and research teams, and explore emerging technologies such as multimodal AI, agent automation, and retrieval-augmented generation.