Top AI Jobs in Singapore

Develop machine learning, statistical, optimization, computer vision, Generative AI, and Agentic AI solutions for smart manufacturing. Analyze operational data, build production-ready AI applications, support cloud deployment and model monitoring, and collaborate with manufacturing, engineering, supply chain, quality, IT, and software teams to improve quality, productivity, reliability, and decision-making.

Produces AI-assisted video, social media, motion graphics, and design content across platforms and regional markets. Responsibilities include scripting, storyboarding, prompting, editing, repurposing assets, channel planning, content optimization, and developing scalable AI workflows. The role evaluates creative tools, trains colleagues, maintains brand consistency, and collaborates with Compliance and Legal to meet financial-services advertising requirements, responsible-AI standards, and regional regulations.

Leads AI engineering teams delivering scalable, production-ready generative AI and agentic solutions. Provides architectural and technical direction, oversees RAG pipelines, vector database integration, model optimization, testing, guardrails, responsible AI, monitoring, and documentation. Collaborates with product and business stakeholders, reviews designs and code, mentors engineers, and shapes technical roadmaps and engineering practices.

Supports enterprise AI initiatives within Legal and Compliance by developing AI-enabled applications, researching emerging technologies, contributing to UI/UX design, creating digital content, and assisting with stakeholder engagement, project communications, and transformation projects. The internship provides exposure to AI solution development, digital transformation, change management, and business innovation in a financial-services environment.

Analytics Intern supporting AI and machine learning model development, quantitative risk analysis, data analytics, model testing, and evaluation of financial-services use cases. The role involves researching emerging techniques, analyzing complex datasets, validating models, and collaborating with quantitative analysts, risk managers, business teams, and technology stakeholders within Group Market Risk Management.

Design, develop, deploy, and optimize enterprise AI applications and single- or multi-agent solutions. Build LLM and RAG pipelines, backend APIs, integrations, evaluation frameworks, security controls, and monitoring. Convert business requirements into scalable AI solutions, industrialize POCs, troubleshoot performance, document architectures, and provide stakeholder communication, consulting, and knowledge transfer. The role requires frequent domestic, regional, and international travel.

Lead customer deployments of Kubernetes and vCluster on bare-metal GPU infrastructure, configuring GPU operators, networking, storage, RDMA, and InfiniBand. Validate production-ready managed Kubernetes environments, transfer operational knowledge, create reusable deployment playbooks, and provide infrastructure feedback to Engineering and Product. Support pre-sales proof-of-value engagements and collaborate with Sales, customer teams, and internal stakeholders.

Leads governance, grant tracking, dashboarding, budget reporting, compliance documentation, and cross-functional coordination for an AI transformation programme. Builds Power BI and Excel dashboards, consolidates finance and operations data, supports vendor onboarding, manages grant administrator relationships, and flags programme risks. The role also applies AI and automation to improve reporting and champions practical AI adoption across Group Finance and business-unit finance teams.

Own product features end-to-end, including architecture, integrations, technical specifications, deployment, testing, and production reliability. Use AI agents as the primary development tool, delegate implementation, critically review generated code, and ensure financial transaction reliability. Work independently as an individual contributor while helping evolve the company’s AI-first development process.

Leads AI-driven workflows, intelligent automation, observability, and autonomous operations across IT Operations and ITSM. Designs AI-assisted incident, problem, and change management capabilities; develops self-healing remediation, dashboards, governance, and operational controls. Supports Digital Command Center activities, including major incident management, executive communications, service governance, and continuous improvement. Requires expertise in AIOps, ServiceNow, Splunk, observability, automation technologies, and enterprise-scale IT operations.

Design, develop, test, deploy, and optimize full-stack applications across frontend and backend systems. Build accessible interfaces, APIs, business logic, and database integrations; write automated tests; review code; troubleshoot complex issues; and contribute to architecture decisions. Collaborate with product, design, and engineering teams in an Agile environment while mentoring junior developers and improving application scalability and performance.

Leads an AI payments business end to end, owning P&L, strategy, product direction, team building, execution, growth, partnerships, revenue, and profitability. Identifies customer and commercial opportunities, collaborates with product, engineering, design, operations, and commercial teams, and launches and scales products while maintaining direct accountability for business results.

Leads an AI-native email app business end to end, owning P&L, strategy, product direction, team building, execution, growth, revenue, partnerships, operations, and profitability. Identifies customer and commercial opportunities, directs product launches, collaborates across product, engineering, design, and commercial teams, and remains accountable for measurable business outcomes.

Build and deploy AI-powered workflows for business teams across operations, retail, finance, engineering, and marketing. Interns identify process problems, scope solutions, prototype with code and AI tools, launch them for users, debug live issues, train teams, and hand over maintainable systems. The role requires evaluating AI output, connecting front ends, APIs, and databases, weighing cost and performance trade-offs, and measuring business impact over a six-month full-time internship.

Originate scientific machine learning methodology focused on PINNs, physics-constrained neural networks, digital twins, uncertainty quantification, Bayesian experimentation, causal ML, and synthetic data. Develop validated research prototypes, acquisition functions, and physics-constrained generative models for product development. Collaborate with storage domain experts, document methodologies for engineering handoff, support design reviews, and contribute to publications or patent disclosures.

Develop multilingual LLMs for Bahasa Indonesia and regional languages by creating high-quality datasets, performing annotation, preprocessing, model training, evaluation, fine-tuning, and alignment. Ensure data quality, language accuracy, and contextual relevance while collaborating with product and engineering teams to deploy safe, reliable AI features. Improve annotation workflows, document technical solutions, and identify impactful AI use cases.

Lead and conduct comprehensive physical security evaluations of semiconductor manufacturing sites across APAC. Identify vulnerabilities in perimeter, access control, CCTV, intrusion detection, and guard operations; assess risks to personnel, continuity, and IP; produce detailed reports and remediation plans; partner with site leadership, suppliers, and cross-functional teams to implement mitigations and improve security standards and technologies.

Develop and deploy applied AI/ML solutions for intelligent transportation, including computer vision, generative AI, agentic AI, data pipelines, and machine learning algorithms. Collaborate with technical and business stakeholders, iteratively refine products with users, evaluate emerging technologies, and establish engineering best practices. The role also involves cloud solution deployment, DevOps practices, testing, version control, and knowledge sharing.

Own and optimize user onboarding end-to-end: define strategy, map journeys, use data and A/B tests to remove friction, balance seamless setup with KYC/risk controls, and drive product adoption at global scale.

Develop and deploy deep learning models for computer vision, voice, and text processing. Responsibilities include data preprocessing, model training and optimization, GPU-based deployment, performance evaluation, unit testing, documentation, and production scaling. The role involves translating business requirements into technical solutions, collaborating with engineering teams, and delivering AI systems for KYC and other financial products.

Lead field-facing engineering to integrate, prototype, and productionize enterprise and sovereign AI platforms. Architect secure, scalable Kubernetes-based deployments, optimize hardware-accelerated inference, upstream field innovations to open-source, mentor engineers, and drive global product feedback loops and SDLC improvements.

Build and operate production AI platform services, backend APIs, integrations, and LLM-powered agent workflows. Own development end to end, including design, coding, testing, deployment, observability, troubleshooting, and quality improvement. Develop reusable platform capabilities, evaluate AI application performance, and collaborate with engineering, product, security, data science, and business teams. The role requires Python backend expertise, cloud operations, databases, LLM applications, and agentic software development.

Design, build, and scale AI-enabled workflows across the firm. Responsibilities include translating business processes into AI systems, developing single-agent and multi-agent workflows with LLM APIs, maintaining prompt and workflow libraries, supporting infrastructure, measuring adoption and impact, documenting best practices, and training internal teams. The role partners with technical and non-technical business units to deploy and refine practical AI automations.

Design, build, deploy, and scale production AI and Generative AI solutions (LLMs, RAG, agentic workflows). Build data pipelines, cloud-based MLOps/LLMOps, and retrieval architectures. Collaborate with stakeholders, embed responsible AI and governance, and develop reusable frameworks, monitoring, and automation for enterprise deployments.

Design, build, and deploy production AI/ML and Generative AI solutions (LLM apps, RAG, agentic workflows). Build data pipelines, MLOps/LLMOps, and cloud-deployed architectures (AWS/Azure/GCP). Collaborate with stakeholders, embed responsible AI and governance, and create reusable assets and best practices to scale AI across the organization.