Top AI Engineer Jobs in Singapore

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.

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.

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.

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

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, 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.

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 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.

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.

Internship role to design and build LLM-powered recommendation and agentic AI systems for financial and Web3 scenarios. Tasks include candidate generation, ranking, tool routing, post-training (SFT/RL), datasets and evaluation pipelines, prototyping agent workflows, and integrating prototypes into production with cross-functional teams.

Develop, train, fine-tune and evaluate multilingual and multimodal LLMs; build scalable multilingual text data pipelines; support large-scale distributed training, experiment tracking and compute optimisation; reproduce and translate research into production improvements; create reusable training/evaluation assets and deployment-ready components; collaborate with academic and industry partners and support community outreach.

Build and maintain end-to-end modules for a multimodal data platform, from frontend UI/UX to backend services and cloud deployment. Implement tooling for evaluations, benchmarks, and data annotation; integrate evaluation frameworks; set up CI/CD, monitoring, and cost-effective cloud infrastructure; collaborate with cross-functional teams.

Operate and maintain the Data Platform across GCP and AWS, ensuring reliability, security, cost and performance. Manage IaC and CI/CD, run incident response and post-mortems, operate central data management (pipelines, storage, catalogues, access controls), improve observability and automation, and apply AI tools to streamline ops and tooling.

Lead design and delivery of complex, scalable software using AI-assisted development. Mentor engineers, implement CI/CD and DevOps practices, establish AI/GenAI best practices and guardrails, validate AI-generated outputs, and contribute to reusable accelerators, presales, and Gen-E2 methodology.

Design, build, and maintain production-grade full‑stack applications using modern frameworks. Implement CI/CD and testing, ensure code quality and security, and use generative AI tools (e.g., Copilot) and prompt engineering as core parts of the development workflow. Validate AI outputs, collaborate with product teams, and contribute to internal Gen-E2 practices and playbooks.