Top AI Engineer Jobs in Singapore

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.

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.

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

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.

Develop and deploy applications using LLMs, fine-tune models, implement advanced workflows, and integrate GenAI pipelines into production systems.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

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.

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.

The role involves pioneering machine learning techniques, developing advanced AI models, building data pipelines, and translating prototypes into products in code analysis.

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.

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 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.

As a Senior AI Engineer, you will utilize AI engineering and delivery expertise to drive transformative projects for clients while building relationships and expanding your knowledge.