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.

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 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.

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

As a Machine Learning Engineer Intern, you will build data pipelines, develop models, and collaborate on data-driven solutions in manufacturing. Gain experience with cloud platforms and MLOps.

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.

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.

Build and productionize AI-native workflows, assistants and automation for a finance super app. Integrate LLMs, data, APIs and business systems; design evaluation, monitoring and fallback flows; prototype, test with users, and deploy reliable AI systems across customer support, CRM, onboarding, claims, payments and operations.

Lead and deliver agentic AI solutions for networking: design RAG and agent architectures, oversee development and deployment, mentor engineers, establish MLOps/DevOps pipelines, optimize performance and governance, and collaborate with networking stakeholders to drive AI strategy and operationalize network automation and anomaly detection.

Build, test and sustain AI agents, RAG/vector search and platform integrations for ETP Hub. Develop retrieval, embedding, indexing and user-facing AI tools, create API connectors and automation scripts, document workflows, and support deployment, monitoring and refinements to improve search, retrieval and operational efficiency.

Develop and deploy enterprise-grade AI and Generative AI solutions, working on applications like LLM and RAG, ensuring operational stability.

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.

Develop, optimize, and deploy machine learning models into production. Build CI/CD pipelines, collaborate with DevOps and engineering, monitor model performance, perform hyperparameter tuning and validation, and research new ML techniques to improve scalability and accuracy.

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.

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.

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

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.

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.