Top AI Engineer Jobs in Singapore

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Leads data-driven industrial optimization projects from requirements gathering through deployment and monitoring. Responsibilities include building scalable data pipelines, integrating industrial data from IoT devices, sensors, test benches, and SCADA systems, performing data quality checks and exploratory analysis, developing machine learning models, creating Power BI and Flask dashboards, supporting digital twin simulations, and developing RAG-based chatbot solutions.

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.

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.

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.

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.

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.

Leads the architecture and development of enterprise AI agents, multi-agent workflows, full-stack applications, backend platforms, and secure integrations. Establishes standards for AI architecture, prompts, tools, telemetry, authentication, observability, governance, scalability, and cost optimization. Designs AWS deployment architectures, leads technical initiatives and workshops, serves as the FDE technical subject matter expert, and mentors engineers while partnering with global business and technology stakeholders.

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.

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.

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 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 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 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 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.

Develop Green AI technologies focused on efficient machine learning, large language models, model optimization, parameter-efficient fine-tuning, retrieval-augmented generation, prompt engineering, and efficient inference. Conduct experiments, benchmark algorithms, analyze performance, contribute to research publications, and collaborate with multidisciplinary teams to translate research into deployable sustainable computing solutions.

Own the end-to-end definition, evaluation, and quality of AI-powered email experiences. Translate model capabilities and technical constraints into product requirements, system decisions, priorities, and measurable outcomes. Partner with ML, backend, and client engineers to improve reliability, safety, latency, cost, usability, and trust. Identify failure modes, establish quality bars, drive experimentation, and guide execution in a fast-moving zero-to-one environment.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs and abstractions connecting AI systems to product experiences, guide a senior engineering team, make architectural trade-offs, and establish strong observability, testing, monitoring, and operational foundations.