Top AI Engineer Jobs in Singapore

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 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.

Design, build, and ship end-to-end production AI applications for smart manufacturing: data pipelines, model training/inference, APIs, web/mobile front-ends, on-prem/edge deployment, MLOps, monitoring, Responsible AI, and integration with robotics and MES.

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

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 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.

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.

The role involves pioneering machine learning techniques, developing advanced AI models, building data pipelines, and translating prototypes into products in code analysis.

Design, build, and deploy end-to-end production AI applications for smart manufacturing: data pipelines, model training/inference, APIs, web/mobile front-ends, digital twins, robotics integration, containerized edge/cloud deployments, GPU inference optimization, Responsible AI, and 3D simulation validation.

Lead design and delivery of end-to-end AI solutions for smart manufacturing, including data pipelines, ML/DL models, LLM and agentic AI integration, full-stack front-end/back-end apps, robotics and AMR integration, digital twin and 3D simulation, containerized cloud/edge deployment, CI/CD, observability, and team mentorship.

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

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.

The role involves driving technical sales strategy for AI solutions, consulting on data architectures, and presenting to C-level executives, requiring extensive customer-facing experience and AI proficiency.

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.

Build and maintain full-stack software and production AI applications for government product teams. Develop LLM workflows, evaluation pipelines, observability, guardrails, testing, and deployment processes. Collaborate with customers on discovery, demos, proofs of concept, architecture, onboarding, integrations, support, and troubleshooting. Maintain technical documentation and improve accessibility, including remediating WCAG issues and learning assistive technologies.

Build and operate governed, audit-ready data foundations for client analytics and AI solutions. Responsibilities include designing ingestion pipelines, lakehouse and warehouse architectures, transformation and semantic layers, data quality, master data, lineage, privacy, residency controls, and AI-ready datasets. The role partners with AI engineers, data scientists, and client stakeholders, contributes reusable engineering accelerators, owns engagement-scale data architecture, and mentors junior resources. It is a hybrid Singapore-based Manager-level position with occasional APAC travel.

Build and operate production backend systems powering AI features. Design inference pipelines, orchestration layers, and model service boundaries while optimizing latency, throughput, caching, batching, and streaming. Monitor reliability through logging, alerting, and incident response, and develop stable APIs for mobile, desktop, frontend, and machine learning integrations. Debug distributed systems under load and continuously improve performance based on production behavior.

Build and ship production-grade AI product features across frontend, backend, and AI integrations. Design reliable multi-step agent workflows with planning, tool use, memory, streaming, failure recovery, and fallback mechanisms. Integrate LLMs, RAG systems, and external tools while improving latency, observability, monitoring, and workflow success rates. Collaborate with ML, backend, and product teams to deliver scalable AI-native experiences.

Provide deep technical support for customers using AI developer products by reproducing, diagnosing, and resolving environment-specific issues across codebases, cloud infrastructure, CI/CD, containers, APIs, and developer tooling. Perform root-cause analysis, communicate findings, escalate with full context, create playbooks and automations, and support release testing to improve reliability and adoption.

Design, build and operate enterprise-scale data and analytics platforms on GCP. Lead architecture, reliability and incident response for BigQuery/Dataproc/Spark workloads, implement IaC and CI/CD, drive SRE/observability, optimize performance and cost, mentor engineers and enforce security and operational best practices.