Top AI Engineer Jobs in Singapore

Develop and maintain enterprise AI platforms and shared services, including reusable frameworks, integrations, APIs, and AI capabilities. Partner with delivery, innovation, engineering, architecture, and business teams to accelerate AI solution deployment. Support platform reliability, scalability, security, governance, privacy, responsible AI, troubleshooting, documentation, and operational excellence. Evaluate emerging technologies such as LLMs, AI agents, MCP, RAG, and cloud AI services while contributing to platform roadmaps and technical best practices.

Build scalable batch and real-time ETL pipelines, data models, and data products for Airwallex’s Customer Data Platform. Develop trusted data foundations across warehouses and cloud infrastructure to support analytics, machine learning, operational decision-making, business intelligence, and AI applications. Partner with globally distributed teams on complex technical work and apply data engineering best practices.

Build scalable batch and streaming ETL pipelines for Airwallex’s Customer Data Platform. Design data foundations, schemas, and models across databases and warehouses to support business intelligence, machine learning, operational decisions, and AI applications. Partner with globally distributed teams on complex technical projects while applying strong problem-solving and collaboration skills.

Design, deploy, and optimize agentic AI workflows and integrations for enterprise customers. Lead implementations from scoping through adoption, connect agents to customer data and systems, establish prompts, evaluations, guardrails, and human oversight, and coordinate engineering, data science, product, and customer success teams. Serve as the primary technical customer partner while managing roadmaps, requirements, delivery risks, and measurable implementation outcomes.

Build and deploy production-grade AI solutions for insurance and financial services, including multi-agent workflows, LLMs, RAG systems, neural networks, and automation pipelines. Responsibilities include model training and optimization, backend development, cloud deployment, production monitoring, regulatory compliance, ethical AI, troubleshooting, code reviews, and collaboration with technical and business stakeholders.

Develop and deploy machine learning models, predictive analytics, and agentic AI solutions for semiconductor manufacturing and engineering. Build scalable data pipelines and ML workflows using large-scale manufacturing datasets to improve yield, quality, reliability, cycle time, and cost. Integrate AI agents with enterprise platforms, mentor Citizen Data Scientists, lead technical initiatives, and support production deployment, MLOps, innovation, and AI adoption across Product Engineering.

Provides senior technology consulting and technical leadership for HPC and AI deployment solutions. Designs enterprise architectures, leads cross-functional implementation teams, resolves complex multi-vendor technology issues, advises senior customer IT leadership, and supports proposals and sales activities. Develops reusable solutions, creates project plans and architecture specifications, presents at technical conferences, mentors consultants, and cultivates strategic customer relationships.

Designs and delivers agentic AI systems that transform complex business processes into autonomous, scalable workflows. Responsibilities include process decomposition, AI architecture, agent and tool orchestration, prompt and context strategy, prototyping, security controls, human-in-the-loop design, testing, cross-functional delivery, value measurement, and mentoring. The role requires collaboration with business, architecture, security, platform engineering, and project management teams through production release.

Designs and delivers agentic AI systems that transform complex manual business processes into secure, scalable autonomous workflows. Responsibilities include process decomposition, AI architecture, agent and prompt design, integrations, prototyping, testing, human-in-the-loop controls, least-privilege security, cross-functional delivery, value measurement, and mentoring. The role requires strong business-process redesign experience alongside hands-on AI/ML and LLM system development.

Leads the redesign of complex business processes into autonomous, agentic AI systems. Responsibilities include process decomposition, AI architecture, agent workflow development, prototyping, integration, security controls, human-in-the-loop design, testing, production delivery, value measurement, and mentoring. The role collaborates with business, architecture, security, platform engineering, and project management teams to deliver scalable generative AI solutions.

Leads a team developing and productionizing predictive, machine learning, deep learning, and GenAI solutions for semiconductor manufacturing and HBM product engineering. Responsibilities include yield and reliability modeling, large-scale data pipelines, MLOps, model monitoring and drift control, distributed analytics, deployment, and engineering decision support. The role also requires mentoring engineers, setting technical direction, collaborating cross-functionally, and translating model outputs into improvements in quality, cost, cycle time, and manufacturing efficiency.

Own the reliability, observability, incident response, and scalability of an internal agentic AI platform. Define SLOs, error budgets, and runbooks; support users; investigate root causes; monitor dependencies; and improve platform engineering practices. Build automation and applications using Python, REST APIs, databases, AWS, Kubernetes, and CI/CD pipelines. Collaborate with stakeholders to ensure AI agents, gateways, LLM proxies, and RAG pipelines deliver high availability, accuracy, and cost efficiency.

Build and operate scalable backend services and APIs for an AI-driven legal platform. Responsibilities include asynchronous message-queue processing, document data pipelines, PostgreSQL schema and query optimization, authentication and authorization, search and vector storage integration, observability, automated testing, CI pipelines, technical design, production support, and on-call participation.

Develop and maintain full-stack AI applications, including user interfaces, backend services, APIs, LLM integrations, agents, and data sources. Translate business requirements into code, support deployment and configuration, gather requirements, prototype solutions, and maintain existing applications. The role requires Python, Git, containerization, frontend development, API development, LLM frameworks, RAG, databases, and awareness of cloud and DevOps practices.

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.

Build and operate secure cloud and on-premises infrastructure for transport AI systems. Responsibilities include GPU compute, model-serving environments, CI/CD, infrastructure-as-code, observability, MLOps support, security compliance, partner integration, incident response, capacity planning, and disaster recovery for live transport operations.

Design, build, and maintain scalable data pipelines, data models, feature stores, and data catalogues to support AI model training and inference. Ensure data quality, lineage, governance, and secure integration of diverse real-time and batch transport data sources. Collaborate with AI architects, infrastructure engineers, and stakeholders to deliver production-grade data products and maintain pipeline reliability.

Designs, develops, deploys, and governs AI-powered products, intelligent workflows, and autonomous multi-agent systems for HR and People organizations. Builds integrations across Workday, ServiceNow, Microsoft platforms, APIs, and enterprise data sources. Develops reusable AI frameworks, orchestration, tool calling, retrieval, monitoring, and evaluation capabilities while ensuring security, privacy, compliance, and responsible AI standards. Partners with business and technology leaders to identify transformation opportunities and deliver scalable solutions across recruiting, onboarding, employee services, learning, talent management, and workforce planning.

Leads technical sales engagements for strategic APAC professional services customers. Designs modern data platforms and AI-ready architectures using Microsoft Fabric, Azure Databricks, Azure databases, Purview, Azure AI, RAG, semantic search, and governed analytics. Influences enterprise architecture decisions, leads proofs of concept and workshops, resolves technical blockers, develops reusable industry solutions, and connects Microsoft Data + AI capabilities to secure production workflows and professional services outcomes.

Develop innovative AI web applications and APIs with the R&D innovation team. Integrate frontier large language models into automotive software, design scalable and usable applications, and perform testing, debugging, and performance optimization. The role also involves applying knowledge of robotics, sensors, actuators, and perception technologies, with opportunities to use ROS/ROS2, Docker, humanoid robotics, and navigation algorithms.

Serves as a technical advisor to customers, leading cloud and AI solution strategy, presales engagements, architecture discussions, demonstrations, and proofs of concept. Partners with Microsoft account teams, engineering groups, and external partners to secure technical wins, address capability gaps, support project delivery, communicate competitive insights, and align Microsoft technologies with customer business, security, and transformation goals.

Design, develop, and deploy enterprise generative AI agents and full-stack AI applications. Translate client business needs into prototypes, production solutions, and implementation strategies. Build agent workflows using RAG, tool calling, memory, orchestration, guardrails, and human-in-the-loop processes. Integrate applications with enterprise systems, APIs, databases, and cloud platforms. Support consulting workshops, technical advisory, pre-sales demonstrations, and production monitoring while communicating effectively with English- and Japanese-speaking stakeholders.

Partner with startup customers to ideate, prototype, deploy, and scale generative AI applications using the OpenAI API. Provide technical guidance, identify business opportunities, collaborate with Sales, Product, and Research teams, contribute to open-source resources, share best practices, and deliver customer feedback internally. The role requires strong software or machine learning engineering experience, startup aptitude, Mandarin fluency, and proficiency in Python, JavaScript, and AI/LLM development.

Lead enterprise technical sales engagements focused on developer productivity, AI-assisted development, and Agentic SDLC adoption. Design secure, scalable architectures, conduct proofs of concept, hackathons, code-with sessions, and architecture workshops, and guide customers through DevOps, cloud-native development, and AI platform adoption. Partner with developers, engineering leaders, and Microsoft product teams to resolve technical blockers, shape solutions, and connect productivity, quality, security, and delivery improvements to business outcomes.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop efficient models and pipelines, improve search relevance and personalization, fine-tune existing models, monitor production performance, and optimize accuracy, latency, cost, and reliability. Collaborate with engineering and product teams to ship measurable improvements for AI-native applications.