Top AI Jobs in Singapore

The Project Officer / Research Associate will conduct research on algorithms for image analysis and AI models for hardware security, including data processing, evaluation, and documentation.

The role involves developing healthcare models, managing data analysis, engaging stakeholders, and supporting research initiatives in hospital-at-home projects.

Lead interdisciplinary research at the intersection of AI and education; design, implement, and evaluate AI-enabled pedagogies, curricula, and assessments; translate research into practice via school collaborations; foster international and industry partnerships; provide teaching, mentorship, and intellectual leadership within NIE/NTU.

The Research Associate will develop AI algorithms for object and defect detection, oversee project budgets, and mentor students.

The Research Associate will conduct research on dexterous hand robotic manipulation for household and manufacturing applications, focusing on developing models and frameworks for effective learning and manipulation.

Design, build, operate and maintain hybrid enterprise AI platforms with automation-first practices. Ensure reliability, scalability, observability, and performance; collaborate with architects, AI engineers, infra and network SMEs; troubleshoot and resolve production issues.

Lead the product roadmap for an AI platform, translate user needs into requirements, and manage a team of product managers in a fast-paced environment.

The Transformation Lead for AI in APAC will manage change and drive adoption of AI tools across the studios, engage stakeholders, and run training programs.

The AI Scientist will develop frameworks to evaluate and audit Reinforcement Learning systems, ensuring safety and performance while leveraging advanced ML techniques.

The intern will develop web applications and dashboards, manage clinical trial data infrastructure, utilize AI tools, and enhance data visualization for decision-making in drug development.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

The AI Architect will develop AI solutions, support the global AI sandbox, provide technical guidance, and optimize AI products while collaborating with various stakeholders.

As an AI Architect, you will design and implement AI solutions, bridge business and technical needs, and lead AI model development within a team. Responsibilities include identifying AI opportunities, maintaining architecture, and optimizing systems while ensuring best practices in MLOps and stakeholder collaboration.

Lead AI strategy, drive company-wide AI adoption, architect production AI systems, and establish best practices and governance for AI initiatives.

Design and deliver production-ready AI and data science solutions across CRM, retail, marketing and operations. Build ML models, GenAI/RAG pipelines, feature engineering and training workflows; collaborate with data engineering to deploy, monitor and optimize models; run experiments and measure business impact while ensuring responsible AI, security and governance.

Paid full-time internship analyzing large-scale WeChat data to identify product opportunities, build dashboards, design scalable data pipelines, and integrate AI tools (LLMs, RAG, AI agents) to automate analysis and support cross-functional product decisions.

Design and deliver production-grade, enterprise-focused AI architecture blueprints and open-source Quickstarts. Collaborate with partners to integrate and benchmark systems, incorporate telemetry, ensure secure and compliant deployments, and optimize runtimes across heterogeneous hardware.

Design and build production-ready enterprise AI architecture blueprints and open-source Quickstarts, integrate and benchmark model serving and telemetry, ensure secure, compliant deployments, and co-develop integrations with partners.

Design production-ready, enterprise-grade AI infrastructure blueprints and lead customer workshops, POCs, and technical advisory. Integrate partner ecosystems, optimize feedback into product roadmaps, and autonomously deliver complex Kubernetes-based, containerized AI/ML solutions while mentoring junior engineers.

Lead design and delivery of scalable AI platform solutions across APAC, scope consulting engagements, architect distributed training/inference and production ML systems, mentor regional teams, align customer needs with Red Hat AI Platform, and represent Red Hat in regional/global AI strategy while traveling up to 40% in APAC.

Lead design, implementation, and productionization of enterprise AI solutions on Red Hat AI Platform and OpenShift across APAC. Deliver end-to-end AI workloads (data pipelines, training, LLM serving, inference), drive pilot-to-production, implement responsible AI controls, mentor team members, and produce architecture and operational documentation.

Design and deliver production-grade enterprise AI architecture blueprints and open-source Quickstarts. Build integrations, benchmarking/evaluation harnesses, and secure, compliant deployment patterns across cloud-native and heterogeneous hardware environments.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

A 12-month graduate programme offering rotations and hands-on projects in Technology or AI & Data Science. Responsibilities include building and deploying software, cloud and infrastructure work, cybersecurity practices, and developing ML/GenAI solutions (LLMs, agents, MLOps). Participants receive structured training, mentoring, and exposure to senior leaders while working on real enterprise problems to deliver scalable, secure, and impactful solutions.

Design, develop, integrate, and maintain highly available, secure, scalable distributed software. Collaborate with engineers, architects, and product teams through the agile lifecycle. Lead feature grooming, estimate technical risks, manage technical debt and third-party component currency, and coach/mentor engineers to deliver high-quality solutions across security, data, networking, and platform domains.