Top AI Engineer Jobs in Singapore

Build scalable batch and real-time ETL pipelines and trusted customer-data foundations for analytics, machine learning, operational decision-making, and AI applications. Develop data models and products across warehouses and cloud infrastructure, using technologies such as Databricks, dbt, Spark, Kafka, and Flink. Collaborate with globally distributed teams on complex technical work.

Build and own scalable batch and real-time ETL pipelines for Airwallex’s Customer Data Platform. Develop trusted data foundations, warehouses, models, and data products supporting analytics, machine learning, operational decisions, business intelligence, and AI applications. Partner with globally distributed teams to deliver complex technical solutions using modern data infrastructure and streaming technologies.

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.

Design, build, and deploy production AI applications for semiconductor manufacturing. Responsibilities include developing full-stack machine learning solutions, APIs, web applications, data pipelines, LLM and agentic AI capabilities, analytics, and integrations with robotics, automation, control systems, and MES platforms. The role also leads deployment, observability, CI/CD, model evaluation, responsible AI, optimization, testing, mentoring, and technical documentation across global manufacturing environments.

Build and deploy full-stack AI applications for smart manufacturing, including data pipelines, machine learning models, APIs, web interfaces, LLM integrations, automation tools, and observability. Develop containerized solutions with Docker and Kubernetes/OpenShift, contribute to CI/CD and MLOps practices, integrate robotics and manufacturing systems, and apply Responsible AI controls. Collaborate with data scientists, software engineers, and manufacturing stakeholders to improve operational decision-making and autonomous operations.

Build and support end-to-end AI applications for semiconductor manufacturing, including data pipelines, machine learning models, APIs, web interfaces, automation tools, and observability. Develop Python and JavaScript/TypeScript solutions, integrate LLMs and agentic AI, connect robotics and manufacturing systems, and deploy containerized applications using Kubernetes or OpenShift. Contribute to CI/CD, Responsible AI, model evaluation, monitoring, testing, documentation, and optimization.

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.

Develops and supports software and AI-enabled IT solutions for semiconductor manufacturing. Responsibilities include requirements analysis, coding, testing, CI/CD, application lifecycle management, global releases, UAT, monitoring, maintenance, system recovery, and L3 operational support. The role requires collaboration with users and engineering teams, occasional cleanroom work, and participation in 24/7 support rotation while applying approved AI tools securely and responsibly.

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.

Senior software engineer builds, deploys, and supports IT applications for semiconductor manufacturing. Owns full application lifecycle, uses AI-assisted tools, implements CI/CD, monitors system performance, coordinates worldwide releases, provides L3 24/7 support, and works in clean-room environments to improve productivity and reliability.

The role involves developing AI and ML solutions for semiconductor workflows, training Citizen Data Scientists, and integrating intelligent agents into engineering processes.

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.

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.

Lead pre-sales technical execution for regional AI security solutions. Partner with sales teams, train sales engineers and channel partners, conduct technical discovery, architecture reviews, demonstrations, proofs of concept, deployments, and customer training. Manage demo environments and benchmarking, support post-sales handoffs, and build relationships with technical decision-makers. Serve as an AI security evangelist while helping customers secure AI applications, infrastructure, and agentic AI deployments.

Serves as a technical advisor to Singapore Government agencies, designing secure cloud, data, analytics, and AI architectures using Microsoft technologies. Leads executive workshops, architecture reviews, demonstrations, proof-of-concepts, and responsible AI transformation initiatives. Partners with sales, engineering, and customers to shape technical strategies, accelerate adoption, influence decisions, and deliver business outcomes. Also contributes to practice development, reusable assets, technical readiness, and peer mentoring.

Build and operate production backend services supporting Supabase AI products, including the MCP server. Responsibilities include service architecture, deployments, scaling, authentication and authorization, observability, reliability targets, on-call incident response, rate limiting, capacity management, and API evolution. The role requires strong TypeScript or Go skills, production backend experience, distributed-systems debugging, and collaboration with Platform and SRE teams in a fully remote environment.

The AI and Robotics Engineer will support AI and robotics standards development across Asia, conduct applied research and system testing, develop technical methods and reports, and deliver training and certification programs. The role engages research institutes, industry, government agencies, and standards bodies; contributes to committees and events; tracks regional developments; and translates findings into standards, briefings, and presentations. The position requires hands-on engineering experience with robotics or applied AI, strong technical communication, and approximately 15–20% travel.

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 software architectures and full-stack prototypes for SAP Business AI research projects. Apply software engineering principles to build enterprise systems and collaborate with multifaceted teams. The 3–6 month full-time internship focuses on innovative AI-related solutions and requires hands-on programming and database development skills.

Develop and deploy production-grade AI and machine learning solutions, including agentic and multi-agent systems, ML pipelines, and advanced models. Design scalable microservices, APIs, event-driven systems, and CI/CD workflows across multiple programming languages. Lead technical architecture, mentor engineers and data scientists, conduct code reviews, and collaborate with product and platform teams to deliver reliable AI services.

Develop and deploy production-grade AI and machine learning solutions, including agentic and multi-agent systems. Design scalable microservices, APIs, event-driven architectures, ML pipelines, and MLOps processes. Lead technical design, mentor engineers and data scientists, conduct code reviews, and collaborate across product and platform teams. Apply advanced AI techniques across areas such as NLP, computer vision, recommendations, and predictive analytics while ensuring system performance, reliability, and maintainability.

Build, deploy, and maintain MLOps pipelines and agentic AI components supporting continuous risk assessments and auditing. Collaborate with audit, data, and engineering teams to identify AI use cases, develop scalable production solutions, evaluate model performance, and monitor reliability. The role also maintains AI component libraries, introduces MLOps innovations, troubleshoots deployments, and communicates technical solutions to stakeholders.

Develop AI-enabled applications for air traffic management by integrating LLMs, RAG, AI agents, tool calling, workflows, operational data, APIs, databases, and enterprise systems. Build backend services, user interfaces, connectors, evaluation and monitoring mechanisms, and end-to-end prototypes. Collaborate with domain experts and air traffic controllers on requirements and testing, while supporting deployments, demonstrations, technical documentation, reports, proposals, and research publications.

Designs, builds, and supports data pipelines, analytics platforms, and Generative AI solutions. Responsibilities include solution delivery, testing, deployment, production monitoring, troubleshooting, incident resolution, and applying data governance, security, and privacy controls. The role collaborates with analysts, data scientists, engineers, and business users while contributing to enterprise data and AI platform improvements.

Design, build, and operate production AI applications, agentic workflows, RAG systems, evaluation frameworks, and data pipelines. Own systems end to end, including architecture, testing, deployment, observability, reliability, and on-call support. Partner with analytics and business teams to deliver practical solutions, improve AI performance, and maintain robust data and context infrastructure. Mentor engineers and contribute to technical design, code reviews, and engineering standards.