Top AI Engineer Jobs in Singapore

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 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.

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 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.

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.

Client-facing engineering role to design, build, and deploy single- and multi-agent LLM systems for public sector delivery. Develop reference architectures, reusable patterns, evaluation frameworks, observability, guardrails, governance, and coach teams to scale agentic capabilities in regulated environments.

The Applied AI Engineer will lead the adoption of AI products, collaborating with clients to integrate AI models and maintain full-stack applications while enhancing customer feedback.

The AI Engineer supports the global AI sandbox, develops AI solutions, conducts R&D, and collaborates with cross-functional teams to deliver high-quality AI products.

Develop and optimize machine learning algorithms, train and evaluate AI models, preprocess data, collaborate on deployment, and drive research innovations in AI.

Develop AI solutions leveraging LLMs, lead development lifecycle, build scalable systems, collaborate across teams, and present to stakeholders.

The AI Engineer will develop AI solutions leveraging LLMs, leading the development lifecycle and collaborating with teams to integrate AI into workflows.

The AI Engineer will develop and deploy AI/ML solutions, focusing on large language models and machine learning to solve engineering challenges, integrating tools into existing workflows, and collaborating with various specialists.

As an Innovation and Applied-AI Engineer, you will develop AI/ML software solutions, prototype new ideas, and implement end-to-end systems within a collaborative, innovative team environment.

The role involves developing and optimizing AI computing infrastructure, focusing on distributed training, compute scheduling, and end-to-end model engineering for gaming applications.

The Senior AI Engineer will develop a retrieval layer for enterprise AI agents, ensuring relevant, permission-aware responses by integrating multiple data sources and designing hybrid retrieval systems. Responsibilities include mentoring, designing pipelines, and building evaluation systems, with a strong focus on retrieval quality and permission management.

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, 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.

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.

Lead design, development, and delivery of production AI systems for insurance workflow automation. Architect inference pipelines, orchestration, routing, caching, observability, and reliability strategies. Collaborate cross-functionally, debug distributed production issues, define engineering standards, and mentor teams to ensure scalable, low-latency AI services.

Design, build, and integrate LLM-powered product features into insurance workflows. Develop prompts, reasoning flows, structured outputs, guardrails, and validation layers. Evaluate model performance with experiments and user feedback, collaborate with backend/product/design teams, and ship reliable, production-ready AI automation and decision-support features.

Lead design, development, and deployment of scalable AI/ML applications and microservices on Kubernetes. Build end-to-end MLOps pipelines, mentor AI engineers, evaluate emerging LLM and GenAI tools, ensure observability and governance, and produce technical documentation and runbooks.

Design, evaluate, and deliver AI solutions for gaming. Build scalable AI services, mentor teams, and communicate technical concepts effectively.