Top AI Engineer Jobs in Singapore

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.

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.

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.

Advises Singapore Government agencies on data modernization, AI transformation, analytics, governance, and cloud adoption. Designs secure enterprise data and AI architectures using Microsoft platforms, leads executive workshops and technical engagements, supports complex sales opportunities, and guides responsible AI adoption in regulated public-sector environments. Collaborates with sales, engineering, partners, and customer stakeholders while contributing reusable assets, technical readiness, and community expertise.

Operate and monitor large-scale datacenter networks for AI workloads, lead incident response, troubleshoot GPU-cluster networking (jitter, NCCL throughput), manage tickets and change control, produce reports, and coordinate with R&D and customers.

Operate and harden Kubernetes-based AI/MaaS production environments; define SLOs, alerting, dashboards, and runbooks; improve rollout safety with canaries and fast rollbacks; drive GPU capacity planning; automate operations with Helm, Argo CD, operators and scripts; and partner to debug incidents from API edge to model workers.

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.

Design and scale high-cardinality telemetry for AI datacenters: integrate hardware exporters into Kubernetes, build eBPF diagnostics, create alerting/dashboard pipelines, enable multi-tenant billing metrics, define observability standards, and lead architecture reviews and mentoring.

Design, deploy, and maintain CSI drivers and GPUDirect Storage integrations for high-performance file systems; implement NVMe caching and optimize IOPS/throughput/latency across containerized storage. Collaborate on RDMA/InfiniBand tuning, define Kubernetes storage policies and multi-tenancy isolation, implement monitoring/alerting, and mentor junior engineers.

Design, implement, and operate advanced batch and gang scheduling systems for large-scale AI/HPC clusters. Build admission control, queueing, topology-aware placement, DRA and custom scheduler plugins, and GPU-sharing/multi-tenancy solutions. Integrate scheduling with bare-metal hardware and storage, resolve contention and deadlocks, and mentor engineers to maintain scalable, reliable orchestration stacks.

Develop, train, evaluate, optimize, and deploy machine learning and deep learning algorithms for intelligent surveillance products. Responsibilities include data preprocessing, feature engineering, model research, algorithm innovation, model compression, inference optimization across hardware platforms, documentation, and collaboration on product integration. The role focuses on computer vision applications including detection, tracking, feature extraction, and recognition.

Develops enterprise Generative AI and agentic AI applications using LLMs, cloud platforms, web frameworks, APIs, RAG, vector search, and AI agent frameworks. Translates client requirements into secure, scalable solutions, facilitates discovery workshops, advises stakeholders, and creates prototypes, demonstrations, and proofs of concept to support consulting engagements and pre-sales activities.