Top AI Engineer Jobs in Singapore

Develop scalable backend services and APIs that integrate generative AI capabilities, LLMs, and machine learning models. Build microservices, data flows, caching, and secure integrations while ensuring reliability through testing, monitoring, observability, and performance optimization. Collaborate with product, design, data science, and ML engineering teams in an agile environment. Mentor junior engineers, conduct code reviews, improve CI/CD practices, and contribute to evolving R&D initiatives.

The role involves developing AI-driven solutions for semiconductor engineering, focusing on GenAI systems, data pipelines, and machine learning models while collaborating with cross-functional teams.

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.

Design, develop, and productionize scalable AI/ML systems, including data and feature pipelines, model deployment, monitoring, drift detection, retraining, and lifecycle management. Automate MLOps workflows, ensure reliability and responsible AI, optimize model performance, and build production-grade AI services and APIs. Partner with product, data science, and platform teams, lead smaller initiatives, review designs and code, and mentor junior engineers.

Develop and maintain global Data and AI infrastructure supporting data engineering and analytics teams. Lead DevOps strategy, evolve scalable and secure cloud infrastructure, manage operational platforms, build observability and detection capabilities, and establish engineering standards across international teams. The role includes working with Kubernetes, Terraform, GitOps, and data streaming platforms such as Kafka, Flink, or Spark.

Lead the Kai generative AI engineering team by defining architecture, building reliable agentic systems, and delivering trusted AI products. Manage and mentor AI, backend, and frontend engineers while driving evaluation, safety, observability, scalability, security, and operational readiness. Partner with Product, Design, Data, Security, Infrastructure, and domain teams to translate business needs into secure customer experiences and reusable AI platforms.

Design and deploy AI agents for payment processing and risk management. Translate business problems into production AI solutions using orchestration frameworks, prompting, RAG, and fine-tuning. Optimize agent reasoning, tool use, and workflow automation, integrate systems with secure backend and frontend platforms, and build evaluation, monitoring, and governance frameworks. The role requires production applied AI/ML experience, strong Python and ML framework skills, and the ability to balance innovation with reliability, scalability, and compliance.

This internship involves assisting in machine learning development, AI automation, data engineering, and exploring innovative projects to enhance test efficiency and engineering productivity.

Design, build, and ship agentic AI applications that answer business questions over governed data. Implement RAG, NL-to-query, tool/function calling, and evaluation/guardrails. Partner with data teams on metrics, models, pipelines, and apply strong data governance and security for financial services.

Lead design and implementation of cloud DevOps strategy for global Data and AI infrastructure. Build observability and detection capabilities, enable self-service tooling for data teams, manage scalable, secure infrastructure, and align DevOps practices and standards across geographies.

Design, build, and operate AI agents that automate SRE, DevOps, and DBA workflows across global infrastructure. Develop safe agent architectures with planning, reasoning, tool execution, error recovery, evaluation, and human escalation. Integrate agents with Kubernetes, Terraform, cloud APIs, databases, monitoring platforms, and CI/CD systems. Collaborate with infrastructure teams to identify automation opportunities and shape Airwallex’s infrastructure AI strategy.

Design, build, deploy, and support end-to-end AI applications for manufacturing: data pipelines, feature engineering, model training/inference, web UIs/APIs, containerized deployments, LLM/RAG integrations, MLOps, observability, and Responsible AI practices.

Develops secure UI and backend services enabling compliant enterprise AI and LLM access for China-based users. Responsibilities include designing APIs and applications, integrating approved LLM endpoints, implementing policy enforcement and access controls, producing architecture artifacts, maintaining AWS services, analyzing operational telemetry, and improving resiliency, security, and coding practices. The role also promotes responsible AI-assisted development, testing, automation, and agile software delivery.

Lead the design, prototyping, development, deployment, and integration of generative AI solutions, including LLMs and GANs. Provide hands-on technical leadership, guide software development practices, mentor engineers, collaborate with product and research teams, and explore emerging technologies such as multimodal AI, agent automation, and retrieval-augmented generation.

The Principal AI Engineer leads the design and operationalization of complex AI systems, architecting multi-agent solutions while ensuring safety, reliability, and performance. They mentor engineers and improve processes and products based on measurable outcomes.

Lead design, build, and operationalize agentic AI platforms and services. Develop orchestration, evaluation, observability, governance, APIs, and backend systems. Improve performance, scalability, and reliability. Provide technical leadership, mentor engineers, and collaborate with product, data science, security, and governance teams to ensure responsible AI deployment across the enterprise.

Lead design and delivery of generative AI agents and architectures, build robust APIs and scalable systems, roadmap AI applications, collaborate cross-functionally, troubleshoot production issues, and drive internal efficiency and customer-facing AI solutions.

Lead end-to-end design and implementation of generative AI solutions and APIs to improve customer-facing and internal automation. Build scalable, high-concurrency backend systems, collaborate with cross-functional teams, set best practices, and mentor engineers while driving deployment and MLOps for production AI products.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Lead data-driven process and test integration initiatives to improve high-value-bits yield (PG2+). Develop models and monitoring for tool performance, equipment prognostics, and test optimization. Prototype solutions with cross-functional teams, deploy best-known-methods to production, and integrate AI-assisted tools to drive continuous improvement and operational excellence.

Designs, develops, deploys, and optimizes enterprise AI and generative AI solutions using LLMs, RAG, intelligent document processing, AI agents, vector databases, and cloud services. Builds secure, scalable applications, APIs, data pipelines, CI/CD and MLOps/LLMOps practices. Partners with product, engineering, business, security, and data teams to deliver AI initiatives, establish governance, evaluate model performance, and drive innovation.

Design and build scalable multi-agent AI systems that automate infrastructure and business workflows. Integrate agents with cloud services, APIs, Kubernetes, telemetry, and observability platforms to perform autonomous remediation, code generation, and system analysis. Develop agent memory and RAG systems, implement evaluation and security guardrails, and establish deployment and governance practices for agentic applications. Collaborate with infrastructure teams to integrate autonomous agents into resilient cloud platforms.

Lead design and development of scalable data infrastructure and self-service tooling for data engineering teams. Provide technical leadership, implement robust systems (Kubernetes/Terraform/GitOps), ensure quality through testing and maintenance, troubleshoot production issues, and collaborate cross-functionally to deliver reliable data platforms.

Lead design and build of high-performance distributed data infrastructure and AI enablement platforms. Optimize systems internals and JVM performance, implement lakehouse and real-time streaming, develop scalable vector indexing and AI knowledge platform, own SDLC for petabyte-scale systems across global regions, drive engineering best practices, and mentor mid-level engineers.

Lead design, build, and operate distributed, large-scale (batch/streaming) data platforms and pipelines. Drive technical decisions, performance optimization, data architecture (lakehouse), tooling standards, third-party integration, and mentor junior engineers to enable company-wide data-driven capabilities.