Top AI Engineer Jobs in Singapore

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

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Develops AI-enabled cybersecurity solutions to improve the firm's security posture and enhance cyber risk products and processes. Responsibilities include analyzing requirements, designing and researching technical solutions, developing and testing software, enforcing SDLC practices, documenting procedures, supporting SRE and operations teams, and evaluating technologies for architectural review.

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.

Design and deliver production-grade enterprise AI architecture blueprints and open-source Quickstarts. Build integrations, benchmarking/evaluation harnesses, and secure, compliant deployment patterns across cloud-native and heterogeneous hardware environments.

Designs, develops, deploys, and optimizes enterprise AI applications using LLMs, RAG, AI agents, document intelligence, vector search, and cloud services. Builds APIs, data pipelines, CI/CD and MLOps/LLMOps practices while ensuring security, governance, privacy, reliability, and cost efficiency. Partners with product, engineering, data, security, and business teams to identify AI opportunities, lead proofs of concept, and deliver scalable production solutions.

Design, build, test, deploy, and support secure AI applications, backend services, APIs, integrations, and reusable platform components. Apply LLMs, retrieval-augmented generation, agentic workflows, prompt engineering, and model evaluation methods. Collaborate with product, architecture, governance, and business teams; maintain testing, CI/CD, monitoring, documentation, and production reliability. Address technical debt, improve platform capabilities, uphold responsible AI and security standards, and mentor engineers across the APAC AI Hub.

Leads the design, development, testing, deployment, and production support of complex enterprise AI applications and platform capabilities. Builds LLM, retrieval-augmented generation, agentic, backend, API, and context-pipeline solutions. Establishes engineering standards for security, privacy, evaluation, CI/CD, observability, resilience, and reliability. Partners with product, architecture, governance, risk, compliance, and global engineering teams, while mentoring engineers and guiding technical decisions.

Develop, test, deploy, and maintain AI-powered applications, backend services, APIs, integrations, and automation. Implement LLM, RAG, prompt engineering, model API, and agentic workflow capabilities. Support experimentation, troubleshoot defects, monitor services, improve reliability and performance, and follow security, privacy, responsible AI, and change-control requirements. Collaborate with engineers, product managers, designers, governance teams, and global stakeholders while contributing maintainable code, documentation, testing, and technical improvements.

Leads technical direction and hands-on development of enterprise AI products, platforms, APIs, integrations, context pipelines, and reusable engineering components across APAC. Builds production-grade LLM, RAG, and agentic solutions while establishing MLOps, LLMOps, security, privacy, testing, observability, reliability, and responsible AI practices. Partners with product, architecture, governance, risk, compliance, cybersecurity, and business teams; resolves complex technical issues, evaluates emerging technologies, mentors engineers, and expands regional AI engineering capabilities.

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.

Provides bilingual Korean and English technical support for enterprise customers using OpenAI products. Troubleshoots API failures, integrations, authentication errors, and production incidents through log analysis, debugging, reproduction, and live incident support. Partners with Engineering, Product, Operations, and Go-to-Market teams to resolve escalations, prevent recurring issues, and improve customer experience. Builds automation and AI-powered workflows using scripting and emerging AI tools. Provides occasional holiday and weekend on-call coverage.

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.

Design, build, and maintain scalable data pipelines, data models, feature stores, and data catalogues to support AI model training and inference. Ensure data quality, lineage, governance, and secure integration of diverse real-time and batch transport data sources. Collaborate with AI architects, infrastructure engineers, and stakeholders to deliver production-grade data products and maintain pipeline reliability.

Develop and enhance toolchain features for neural processing unit architectures, enabling customers and partners to optimize and port AI models. Responsibilities include compiler development, code generation, program analysis, pipeline optimization, model-layer modification, automation, debugging, collaboration with inference and application engineering teams, and customer support.

Advises Singapore Government agencies on data modernization, AI transformation, analytics, governance, and cloud adoption. Designs secure enterprise data and AI architectures using Microsoft platforms, leads executive workshops and technical engagements, supports complex sales opportunities, and guides responsible AI adoption in regulated public-sector environments. Collaborates with sales, engineering, partners, and customer stakeholders while contributing reusable assets, technical readiness, and community expertise.

Design and deploy optimized on-device AI models for gaming, biosensing, and peripheral applications. Develop real-time biosignal and sensor-processing systems, optimize models through quantization and pruning, write production-quality C++ inference runtimes, create APIs, and profile performance across CPU, GPU, and NPU targets.

Develop AI-enabled solutions and full-stack applications that address business needs. Responsibilities include process analysis, automation, stakeholder engagement, prototyping, development, deployment, production ownership, and continuous improvement. The role requires React, TypeScript, Node.js, PostgreSQL, and API development, with advantageous experience in Python, Docker, GraphQL, C#, LLM APIs, embeddings, and AI 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.

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

Lead design, development and deployment of enterprise AI/ML platforms and production models. Act as AI platform SME, build reusable libraries, data/ML pipelines, tools and UIs, collaborate with data scientists, IT and business stakeholders, and drive adoption of GenAI and best practices across the bank.

Own product areas within AI stockbroking and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; use research, feedback, metrics, and production issues to guide decisions; launch features, monitor performance, and drive iteration while balancing speed, customer experience, business impact, compliance, and operational complexity.

Own AI Investing product areas across financial services, defining user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial journeys. Use data, research, feedback, experiments, and production issues to improve products, monitor performance, and drive iterations while balancing speed, user experience, business impact, compliance, and operational complexity.

Own product areas within KIRA’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; and use data, feedback, experiments, and production insights to continuously improve products.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; and use data, research, feedback, and production issues to drive continuous improvement.