Top AI Engineer Jobs in Singapore

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.

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 Senior AI Engineer designs and develops scalable AI/ML systems, operationalizes models, ensures reliability and observability, and mentors junior engineers.

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.

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

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 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 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 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 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 productionize GenAI and ML solutions for semiconductor engineering: large-scale data pipelines, LLM/RAG and agentic workflows, model training/inference, analytics, deployment, monitoring, and cross-functional collaboration to improve quality, cost, and engineering productivity.

Develop advanced calibration, localization, and mapping algorithms for autonomous robots, ensuring high-quality code delivery and collaboration across teams.

Develop and implement AI solutions for game development, enhance integration with game engines, and collaborate with teams to analyze player engagement.

Build and ship production AI systems: agents, RAG/LLM apps, tool-calling and automations for onboarding, KYC, risk, support and operations. Work with product and engineering, design for accuracy, latency, cost, security and compliance, and convert manual processes into scalable AI-enabled workflows.

Build and ship production-grade AI agents, LLM apps, RAG systems and automation workflows for a fintech neobank. Integrate retrieval, tool calling, evaluations and guardrails to improve onboarding, KYC, risk review, support, CRM and operational processes while meeting constraints of accuracy, latency, cost, security and compliance.

Design, build, and deploy production AI/ML and Generative AI solutions (LLM apps, RAG, agentic workflows). Build data pipelines, MLOps/LLMOps, and cloud-deployed architectures (AWS/Azure/GCP). Collaborate with stakeholders, embed responsible AI and governance, and create reusable assets and best practices to scale AI across the organization.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and implement scalable LLM-powered applications, microservices, and APIs. Build AI agents, RAG-enabled semantic search, vector DB integration, CI/CD pipelines, and guardrails. Collaborate with stakeholders, own backend and full-stack development, and support DevOps, testing, and production reliability.

The AI Engineer will lead AI projects, train apprentices, develop AI technologies, manage recruitment, and support industry activities.

As a Fullstack AI Engineer, you will design and maintain AI-powered applications, develop Proof-of-Concepts, enhance AI frameworks, and collaborate on production-grade systems in a cloud-native environment.

As an AI Engineer, you'll build prototypes, design system architectures, write code, integrate AI services, and manage deployments, ensuring software solutions meet public sector needs.