Top AI Engineer Jobs in Singapore

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 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.

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.

Lead the design, development, and integration of AI-powered applications while ensuring code quality and collaborating across teams. Ideal for engineers focused on automation and secure application engineering.

Design and deliver production-ready AI and data science solutions across CRM, retail, marketing and operations. Build ML models, GenAI/RAG pipelines, feature engineering and training workflows; collaborate with data engineering to deploy, monitor and optimize models; run experiments and measure business impact while ensuring responsible AI, security and governance.

Build production AI-native systems for BJAK's neobank: AI agents, RAG and retrieval, tool-calling and automations to improve onboarding, KYC, support, risk review, CRM and operations while addressing accuracy, latency, cost, security and compliance.

Design, develop, and maintain scalable risk applications, microservices, and APIs using Java/Python and related frameworks. Collaborate with stakeholders, apply OOAD/OOP design patterns, implement CI/CD and DevOps practices, manage SDLC, and ensure code quality and documentation.

As a Principal AI Engineer, you will lead the architecture and deployment of large-scale conversational chatbot systems, optimizing their performance for enterprise and consumer use cases.

Build and deploy production agent applications integrated with internal databases, APIs, and data sources. Create reusable, secure starter templates and partner with business teams to identify, prioritize, and iterate on agent use cases while following and improving firm standards for deployment, security, and observability.

Lead design and delivery of production LLM-powered agent systems and firm-wide agent standards. Build reference agent apps, connect agents to internal data/APIs, establish security and governance for AI, adopt deployment and observability infrastructure, and partner with stakeholders to identify high-value use cases.

Lead and coordinate AI projects across teams, design and deploy AI solutions for land transport, uplift internal AI capabilities, engage industry partners, and drive experimentation in ML, generative and agentic AI.

Build and productionize cutting-edge GenAI applications (RAG, multi-agent systems, Text2SQL, fine-tuning), deploy and optimize production ML systems on cloud platforms, advise and collaborate with customers and cross-functional product and engineering teams, and represent Databricks externally at conferences.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

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, develop, and deploy agentic AI solutions to optimize network automation, predictive maintenance, anomaly detection, and intelligent network management. Collaborate with networking stakeholders to translate goals into technical requirements, ensure compliance with performance, risk, and regulatory standards, research emerging agentic AI trends, and provide troubleshooting, QA, and documentation for production AI systems.

Design and build company-wide AI knowledge infrastructure and context layers, develop scalable LLM application architecture (RAG, vector DBs, prompt workflows), own end-to-end delivery of internal AI tools, optimize performance and inference, evaluate/integrate AI tooling, and mentor junior engineers while translating business needs into technical roadmaps.

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.