Top AI Engineer Jobs in Singapore

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.

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, 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.

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.

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 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.

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.

Operate and optimize multi-cloud GPU inference platform for LLMs, build API services and CI/CD, manage AI clusters with IaC, improve observability, automate operations and build AI-assisted internal tools.

As a Robotics AI Engineer Intern, you'll work on humanoid robotics software features, specializing in Vision-Language Model integration, simulation environment setup, or 3D Gaussian Splatting. You'll collaborate with senior engineers, develop pipelines, and configure digital twins.

Own design, build, and productionization of LLM-powered agentic systems for materials R&D: tool orchestration, memory and MCP integration, multi-agent skills, evaluation frameworks, and production reliability/observability. Advise product and platform teams and measure system quality with domain experts.

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.

Lead AI Engineer will design and deploy advanced AI and agentic LLM systems, lead and mentor an AI/data engineering team, build MLOps pipelines and cloud deployments, produce client proposals and technical thought leadership, and drive model lifecycle, evaluation, and responsible-AI practices.

The Senior AI/ML Engineer will design, develop, and deploy language models for African languages, focusing on NLP applications and cultural context.

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.

As an Applied AI Engineer, you will develop AI features, improve model behavior, debug production issues, and collaborate on systems that deliver reliable AI solutions.

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 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.