Top AI Engineer Jobs in Singapore

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.

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 and operate enterprise-scale AI platforms, services, data pipelines, and ML workflows. Responsibilities include data ingestion, feature engineering, model deployment, evaluation, monitoring, governance, cloud migration, CI/CD automation, and observability. Collaborate with data scientists, product teams, and platform engineers to deliver secure, scalable production systems using Python, Spark, SQL, Hadoop tools, and cloud technologies.

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

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.

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.

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.

Leads the architecture and delivery of production AI products for autonomous semiconductor manufacturing. Responsibilities span data engineering, machine learning, GenAI, full-stack applications, robotics integration, digital twins, simulation, and edge-to-cloud deployment. The role also owns CI/CD, observability, model evaluation, Responsible AI, performance optimization, technical mentorship, and cross-functional delivery across software, manufacturing, robotics, and infrastructure teams.

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.

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.

Develop realistic backend coding challenges, including feature implementations and bug fixes, for AI training projects. Design deterministic verifiers, automated tests, and validation criteria that distinguish correct from incorrect solutions. Evaluate code for correctness, reliability, maintainability, security, and performance while identifying edge cases and bottlenecks. Work independently with backend technologies and refine tasks based on project requirements and quality standards.

Designs production-ready AI/ML architecture blueprints and open-source Quickstarts for enterprise customers. Builds Python-based solutions using deep learning, cloud-native platforms, MLOps, model serving, evaluation harnesses, telemetry, and secure regulated deployment patterns. Collaborates with engineering partners to validate integrations, optimize performance across heterogeneous hardware, and address privacy, compliance, data residency, and network isolation requirements. The role emphasizes autonomous technical delivery without people management or formal project coordination.

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.

Develop and implement agentic AI for cybersecurity, assess AI security in AIOps pipelines, optimize models for deployment, and monitor models in operations. The internship provides experience with AI model development, machine learning workflows, MLOps, and software development using Python or C++.

Designs, prototypes, builds, and supports production AI agents and assistants for internal business teams. Translates ambiguous business needs into technical designs, selects build-versus-configure approaches, architects integrations and access controls, and develops Python-based APIs and automations. Integrates LLM observability and evaluation tools, presents solutions to stakeholders and review boards, and iterates on live systems based on user feedback.

Own end-to-end delivery of AI and autonomous robotics inspection solutions for maritime and offshore clients. Lead technical discovery, solution design, project delivery, client advisory, stakeholder alignment, vendor collaboration, and user adoption. Direct computer vision, perception, sensor fusion, SLAM, anomaly detection, cloud AI, MLOps, security, and data governance practices while reviewing architecture, validating model performance, supporting proposals, and guiding production deployment.

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, evaluate, deploy, and monitor LLM-powered agentic systems for regulatory workflows. Develop LangGraph/LangChain agents, evaluation and observability pipelines, RAG and vector-search systems, and production LLMOps practices. Integrate AI services into Node and React applications while optimizing prompts for cost, latency, and accuracy. Collaborate with product, engineering, and regulatory stakeholders, and mentor colleagues on agent design and evaluation.

Support ML engineers operating production training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through automation, tooling, documentation, runbooks, and operational processes. The role is remote in the Philippines or Singapore and follows a Sunday-Wednesday APAC shift.

Design and develop production MCP and AI Gateways that connect enterprise systems, AI agents, and LLM providers. Build high-throughput network services with routing, streaming, authentication, observability, concurrency, and tenant isolation. Optimize PostgreSQL-backed applications, profile services under load, and operate containerized deployments with Kubernetes. Integrate LLM APIs at the protocol level while improving reliability, latency, security, and cost.