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.

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.

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.

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.

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 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.

Designs and operates AI evaluation and benchmarking frameworks for LLM, RAG, and agentic systems. Responsibilities include creating benchmark suites and datasets, integrating biomedical data, developing regression pipelines, capturing human feedback, analyzing errors and drift, maintaining reproducibility, and producing dashboards and reports. The engineer collaborates with scientists, AI engineers, architects, and governance teams to support model selection, release gates, and continuous workflow improvement.

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.

Productionize, operate, and improve machine-learning and AI workloads on AWS and Databricks. Build reliable data, feature, and MLOps pipelines; convert prototypes into tested production services; optimize Spark, SQL, cloud performance, and costs; implement CI/CD, governance, security, observability, evaluation, retraining, and rollback processes; support recommendation and search systems; and document solutions while coaching teams on sustainable operations.

Design and build agentic AI systems for fraud-risk discovery and detection engineering in a Singapore Government environment. Responsibilities include agent architecture, LLM workflows, data and analytics integration, production engineering, CI/CD, evaluation, responsible-AI guardrails, monitoring, documentation, and knowledge transfer. The role requires integrating AWS, PySpark, enterprise data platforms, and MLOps practices while collaborating with data scientists, analysts, business users, and fraud-domain officers.

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.

Build and deploy AI agents and machine learning solutions using large language models, tool-use frameworks, and SAP Business AI. Validate proof-of-concepts for accuracy, latency, cost, and safety before production. Explore emerging AI technologies, contribute to software development lifecycle activities, and collaborate with global product and IT delivery teams.

Develop and support cloud-native applications, microservices, APIs, CI/CD pipelines, containerized workloads, and Kubernetes platforms. Manage multi-cloud infrastructure across AWS, Azure, and GCP while implementing monitoring, observability, automation, and production support practices. Support AI/ML infrastructure, GPU clusters, model-serving platforms, and emerging technologies. Collaborate across engineering, security, operations, and AI teams to deliver scalable, secure, reliable platform solutions.

Lead AI and machine learning projects, develop production LLM and agentic applications, mentor apprentices, maintain AI technologies, and support AI training programmes. Responsibilities include end-to-end project delivery, use-case scoping, applied machine learning, MLOps, cloud pipelines, technical research, curriculum development, workshops, and communicating complex concepts to technical and non-technical audiences.

Develop and deploy AI solutions for public- and private-sector clients while supporting AI governance, data protection, fairness, and transparency. Apply machine learning across NLP, computer vision, recommendation systems, reinforcement learning, and forecasting. Build end-to-end AI systems using modern cloud, MLOps, software engineering, and infrastructure tools. Collaborate with clients, colleagues, and partners, continuously expand technical expertise, and contribute to transformative digital initiatives.

Leads technical pre-sales engagements for Microsoft AI workforce solutions, including demos, pilots, architecture design, adoption planning, and technical decision-making. Partners with sales, customer success, engineering, and partners to generate pipeline, remove blockers, secure competitive wins, and support Copilot and Agents adoption. Builds secure, scalable architectures, manages deal hygiene, develops executive relationships, and provides feedback to product and sales leadership.