Top AI Engineer Jobs in Singapore

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.

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 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.

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.

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.

Partner with business functions to identify AI use cases, design and build AI-powered solutions (LLMs, automation, prompt engineering, low-code), run pilots, measure impact, and present results to stakeholders while translating between technical and business needs.

Lead development and deployment of agentic AI and RAG solutions for network automation, predictive maintenance, anomaly detection, and intelligent network management. Architect scalable network AI systems, drive AI strategy with networking teams, mentor engineers, implement MLOps/CI-CD, and ensure performance, governance, and regulatory compliance.

As a Principal AI Engineer, you will lead the architecture and deployment of large-scale conversational chatbot systems, optimizing their performance for enterprise and consumer use cases.

Build production AI agents, LLM apps, RAG and retrieval-based systems, tool-calling and automations for fintech workflows (onboarding, KYC, risk, CRM). Ship, evaluate, and improve reliable AI-enabled systems with product and engineering teams, addressing latency, cost, security and compliance.

Client-facing engineering role to design, build, and deploy single- and multi-agent systems for public sector delivery. Develop reference architectures, evaluation frameworks, guardrails, governance, and reusable patterns; integrate agent tooling and orchestration; coach delivery teams and ensure safe, observable, production-ready autonomous and semi-autonomous agents.

Design, build, and deploy single- and multi-agent agentic systems for public sector clients. Develop reference architectures, evaluation frameworks, observability, guardrails, governance, and reusable patterns. Integrate appropriate technologies, coach delivery teams, and ensure safe, measurable agent behavior in regulated environments.

Client-facing engineer who designs, builds, and deploys single- and multi-agent systems using LLMs for public sector delivery. Develops reference architectures, guardrails, evaluation frameworks, observability, governance, and reusable patterns; integrates agent/harness/orchestration tools; and coaches delivery teams on agentic engineering best practices.

Lead design and delivery of production LLM-powered agent systems and firm-wide agent standards. Build reference agent apps, connect agents to internal data/APIs, establish security and governance for AI, adopt deployment and observability infrastructure, and partner with stakeholders to identify high-value use cases.

Lead and coordinate AI projects across teams, design and deploy AI solutions for land transport, uplift internal AI capabilities, engage industry partners, and drive experimentation in ML, generative and agentic AI.

Build and productionize cutting-edge GenAI applications (RAG, multi-agent systems, Text2SQL, fine-tuning), deploy and optimize production ML systems on cloud platforms, advise and collaborate with customers and cross-functional product and engineering teams, and represent Databricks externally at conferences.

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 build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

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.

As a Solutions Architect at CoreWeave, you'll lead the technical relationship with customers to optimize AI workloads on cloud infrastructure, focusing on Kubernetes and HPC environments.