Top AI Jobs in Singapore

Lead development and maintenance of the banks Data and AI risk management framework, set data risk appetite and controls, translate regulatory expectations, codify standards, and ensure consistent definition and reporting of data risks and control gaps across the Group.

Lead development and refinement of the banks AI risk framework; apply frameworks to use cases; review higher-risk AI applications; liaise with business, technology, and control owners; track remediation and emerging risk themes; prepare governance materials and monitor regulatory developments.

Lead integration of AI into the chip development lifecycle: design and import AI tools/processes, localize and promote adoption, train engineers, monitor effectiveness, coordinate cross-functional teams, and continuously optimize AI-enabled workflows to improve R&D efficiency and competitiveness.

Develop and maintain scalable backend systems, implement APIs, manage databases, and collaborate with stakeholders to deliver AI solutions.

The AI Governance Manager will lead AI governance initiatives, ensuring ethical AI system deployment while overseeing compliance, risk assessment, and stakeholder engagement.

The Applied AI Transformation Manager roles involve strategic advising, program delivery, and enhancing productivity for customers using AI technologies.

As a Product & AI Engagement Trainee, you will boost user engagement through experiments, analyze behavior, support partner integration, and conduct user research for product improvement.

Lead delivery and scaling of generative AI data programs across multiple projects: capacity planning, vendor management, quality consistency, process automation, financial control, risk mitigation, client reporting, and team enablement.

Lead and scale portfolios of generative AI data programs (collection, labeling, evaluation). Manage capacity planning, vendor SLAs, quality consistency, process automation, budgets, risk, stakeholder reporting, and coach PMs/coordinators while ensuring data security and compliance.

Lead end-to-end delivery of enterprise IT systems using AI tools, managing project scope, schedules, vendors, integrations, testing, deployment, operations, and governance. Engage stakeholders, gather and document requirements, design solutions, ensure security/compliance, and support procurement, incident resolution, and continuous improvement for mission-critical platforms.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Design and build AI-powered developer platforms, agent orchestration, RAG/retrieval systems, and CI/CD-integrated workflows to automate code generation, testing, deployment, and engineering productivity. Drive AI adoption, define best practices, and measure impact across Group Wealth Engineering.

Design, develop, and evaluate AI-driven solutions for sustainable living. Collect and preprocess multimodal datasets, build data pipelines and experiments, analyze model performance, iterate on models, and collaborate on publications, reports, and demonstrations.

Design, fine-tune and deploy AI/ML (including LLM) solutions for infrastructure and built-environment workflows using geospatial and 3D data. Build scalable training pipelines and scripts in Python, integrate tools with CAD/GIS/BIM software, ensure regulatory alignment and collaborate with engineers and GIS/BIM specialists to operationalize AI solutions.

Internship role to design and build LLM-powered recommendation and agentic AI systems for financial and Web3 scenarios. Tasks include candidate generation, ranking, tool routing, post-training (SFT/RL), datasets and evaluation pipelines, prototyping agent workflows, and integrating prototypes into production with cross-functional teams.

Support multi-asset investment strategy through research across macro, equities, fixed income and alternatives; monitor macro and market developments; assist with model maintenance, data gathering and scenario analysis; produce investment notes, outlooks and client materials; collaborate to design, build and deploy agentic AI tools, document architectures and best practices, and liaise with technology stakeholders.

Develop, train, fine-tune and evaluate multilingual and multimodal LLMs; build scalable multilingual text data pipelines; support large-scale distributed training, experiment tracking and compute optimisation; reproduce and translate research into production improvements; create reusable training/evaluation assets and deployment-ready components; collaborate with academic and industry partners and support community outreach.

Build and maintain end-to-end modules for a multimodal data platform, from frontend UI/UX to backend services and cloud deployment. Implement tooling for evaluations, benchmarks, and data annotation; integrate evaluation frameworks; set up CI/CD, monitoring, and cost-effective cloud infrastructure; collaborate with cross-functional teams.

Operate and maintain the Data Platform across GCP and AWS, ensuring reliability, security, cost and performance. Manage IaC and CI/CD, run incident response and post-mortems, operate central data management (pipelines, storage, catalogues, access controls), improve observability and automation, and apply AI tools to streamline ops and tooling.

Lead and own technical direction for engineering teams, embedding AI/GenAI into development workflows. Provide full-stack architecture and code guidance, enforce GenAI best practices, mentor engineers, ensure delivery quality via CI/CD and testing, collaborate with clients and sales, and scale reusable assets using the Gen-E2 methodology.

Lead design and delivery of complex, scalable software using AI-assisted development. Mentor engineers, implement CI/CD and DevOps practices, establish AI/GenAI best practices and guardrails, validate AI-generated outputs, and contribute to reusable accelerators, presales, and Gen-E2 methodology.

Own technical direction and delivery for engineering teams, embed AI-native workflows (Gen-E2), define GenAI best practices, provide architecture and code review, ensure CI/CD and quality, mentor and grow engineers, collaborate with clients and sales, and contribute reusable assets and thought leadership.

Lead discovery workshops, elicit and document business, functional and non-functional requirements, map and re-engineer processes, create user stories and acceptance criteria, manage product backlogs and requirements delivery across SDLC, collaborate with cross-functional teams, validate AI-generated artefacts, and support stakeholder alignment and client relationships.

Design, build, and maintain production-grade full‑stack applications using modern frameworks. Implement CI/CD and testing, ensure code quality and security, and use generative AI tools (e.g., Copilot) and prompt engineering as core parts of the development workflow. Validate AI outputs, collaborate with product teams, and contribute to internal Gen-E2 practices and playbooks.

Design, develop, and support AI-enabled automation and integration solutions across the SEAO region. Build Python- and RPA-based automations, integrate systems via APIs, troubleshoot and maintain solutions, run AI pilots and POCs, document implementations, and collaborate with stakeholders to promote responsible, scalable AI adoption and continuous improvement.