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.

Supports semiconductor manufacturing equipment through hands-on troubleshooting, data analysis, predictive maintenance, automation, and AI-driven optimization. The role monitors equipment performance, identifies trends, improves reliability and productivity, collaborates on smart manufacturing initiatives, maintains technical documentation, and promotes EHS&S compliance. Normal, swing, day, and night shifts may be required. Candidates without prior experience are welcome and will receive comprehensive training.

Build and deploy full-stack internal tools, AI-powered agents, and copilots that automate workflows across APAC Data & Analytics. Integrate systems through APIs, collaborate with data, marketing, commercial, and finance teams, and manage delivery from prototyping through production support on GCP. Ensure solutions meet enterprise architecture, security, and coding standards.

Build and evaluate benchmark datasets for LLM systems processing patent documents and deal-sourcing data. Responsibilities include reviewing and refining annotations, running blind-test evaluations, measuring extraction reliability, identifying hallucinations and edge cases, cataloging schemas, and preparing quality-acceptance reports. The role offers direct engineering supervision, stakeholder-facing projects, and deployment experience.

Build and ship internal AI-enabled applications, including data-review, deal-signal, service-monitoring, and patent/IP-management tools. Develop agents and automations connecting internal systems, integrate LLM-based judgment steps, write reliability tests, and contribute to full-stack development and deployment. Work directly with stakeholders and learn relevant patent and intellectual-property domains.

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.