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.

Design, validate, and deploy AI algorithms for digital health projects. Lead technical innovation, support data platform development and retrospective analysis, collaborate with clinicians and biostatisticians, publish findings, prepare technical reports, and mentor junior AI researchers.

As an AI Application Engineer, you will debug and improve platforms, develop AI tools and proof-of-concepts, and collaborate across teams to enhance AI systems.

Design, build, deploy and govern AI solutions (LLMs, agentic systems, MLOps). Collaborate with clients and partners, deliver end-to-end AI projects, and continuously upskill in AI engineering and deployment best practices.

Design, build, deploy, and scale production AI and Generative AI solutions (LLMs, RAG, agentic workflows). Build data pipelines, cloud-based MLOps/LLMOps, and retrieval architectures. Collaborate with stakeholders, embed responsible AI and governance, and develop reusable frameworks, monitoring, and automation for enterprise deployments.

Lead engineering for an AI-native Web3 product: ship production features, turn AI-assisted prototypes into scalable systems, own architecture/security/quality, direct AI coding agents, define engineering standards, and collaborate with founders on product and technical strategy.

Design, build, and operate backend services and data pipelines to ingest, process, enrich, and deliver large scientific, media, and geospatial datasets. Implement REST APIs, event-driven and streaming systems, and Kubernetes-based production infrastructure to support machine learning and agentic AI applications. Collaborate with scientists and ML engineers to translate requirements into scalable, reliable solutions.