Top AI Engineer Jobs in Singapore

Build and deploy production-grade AI solutions for insurance and financial services, including multi-agent workflows, LLMs, RAG systems, neural networks, and automation pipelines. Responsibilities include model training and optimization, backend development, cloud deployment, production monitoring, regulatory compliance, ethical AI, troubleshooting, code reviews, and collaboration with technical and business stakeholders.

Develop and deploy machine learning models, predictive analytics, and agentic AI solutions for semiconductor manufacturing and engineering. Build scalable data pipelines and ML workflows using large-scale manufacturing datasets to improve yield, quality, reliability, cycle time, and cost. Integrate AI agents with enterprise platforms, mentor Citizen Data Scientists, lead technical initiatives, and support production deployment, MLOps, innovation, and AI adoption across Product Engineering.

Develops secure UI and backend services enabling compliant enterprise AI and LLM access for China-based users. Responsibilities include designing APIs and applications, integrating approved LLM endpoints, implementing policy enforcement and access controls, producing architecture artifacts, maintaining AWS services, analyzing operational telemetry, and improving resiliency, security, and coding practices. The role also promotes responsible AI-assisted development, testing, automation, and agile software delivery.

Provides senior technology consulting and technical leadership for HPC and AI deployment solutions. Designs enterprise architectures, leads cross-functional implementation teams, resolves complex multi-vendor technology issues, advises senior customer IT leadership, and supports proposals and sales activities. Develops reusable solutions, creates project plans and architecture specifications, presents at technical conferences, mentors consultants, and cultivates strategic customer relationships.

Designs and delivers agentic AI systems that transform complex business processes into autonomous, scalable workflows. Responsibilities include process decomposition, AI architecture, agent and tool orchestration, prompt and context strategy, prototyping, security controls, human-in-the-loop design, testing, cross-functional delivery, value measurement, and mentoring. The role requires collaboration with business, architecture, security, platform engineering, and project management teams through production release.

Designs and delivers agentic AI systems that transform complex manual business processes into secure, scalable autonomous workflows. Responsibilities include process decomposition, AI architecture, agent and prompt design, integrations, prototyping, testing, human-in-the-loop controls, least-privilege security, cross-functional delivery, value measurement, and mentoring. The role requires strong business-process redesign experience alongside hands-on AI/ML and LLM system development.

Leads the redesign of complex business processes into autonomous, agentic AI systems. Responsibilities include process decomposition, AI architecture, agent workflow development, prototyping, integration, security controls, human-in-the-loop design, testing, production delivery, value measurement, and mentoring. The role collaborates with business, architecture, security, platform engineering, and project management teams to deliver scalable generative AI solutions.

Leads a team developing and productionizing predictive, machine learning, deep learning, and GenAI solutions for semiconductor manufacturing and HBM product engineering. Responsibilities include yield and reliability modeling, large-scale data pipelines, MLOps, model monitoring and drift control, distributed analytics, deployment, and engineering decision support. The role also requires mentoring engineers, setting technical direction, collaborating cross-functionally, and translating model outputs into improvements in quality, cost, cycle time, and manufacturing efficiency.

Designs, develops, deploys, and governs AI-powered products, intelligent workflows, and autonomous multi-agent systems for HR and People organizations. Builds integrations across Workday, ServiceNow, Microsoft platforms, APIs, and enterprise data sources. Develops reusable AI frameworks, orchestration, tool calling, retrieval, monitoring, and evaluation capabilities while ensuring security, privacy, compliance, and responsible AI standards. Partners with business and technology leaders to identify transformation opportunities and deliver scalable solutions across recruiting, onboarding, employee services, learning, talent management, and workforce planning.

Build and operate enterprise-scale AI platforms, services, data pipelines, and ML workflows. Responsibilities include data ingestion, feature engineering, model deployment, evaluation, monitoring, governance, cloud migration, CI/CD automation, and observability. Collaborate with data scientists, product teams, and platform engineers to deliver secure, scalable production systems using Python, Spark, SQL, Hadoop tools, and cloud technologies.

Lead data-driven process and test integration initiatives to improve high-value-bits yield (PG2+). Develop models and monitoring for tool performance, equipment prognostics, and test optimization. Prototype solutions with cross-functional teams, deploy best-known-methods to production, and integrate AI-assisted tools to drive continuous improvement and operational excellence.

Build and deploy full-stack AI applications for smart manufacturing, including data pipelines, machine learning models, APIs, web interfaces, LLM integrations, automation tools, and observability. Develop containerized solutions with Docker and Kubernetes/OpenShift, contribute to CI/CD and MLOps practices, integrate robotics and manufacturing systems, and apply Responsible AI controls. Collaborate with data scientists, software engineers, and manufacturing stakeholders to improve operational decision-making and autonomous operations.

Design, build, and deploy production AI applications for semiconductor manufacturing. Responsibilities include developing full-stack machine learning solutions, APIs, web applications, data pipelines, LLM and agentic AI capabilities, analytics, and integrations with robotics, automation, control systems, and MES platforms. The role also leads deployment, observability, CI/CD, model evaluation, responsible AI, optimization, testing, mentoring, and technical documentation across global manufacturing environments.

Leads the architecture and delivery of production AI products for autonomous semiconductor manufacturing. Responsibilities span data engineering, machine learning, GenAI, full-stack applications, robotics integration, digital twins, simulation, and edge-to-cloud deployment. The role also owns CI/CD, observability, model evaluation, Responsible AI, performance optimization, technical mentorship, and cross-functional delivery across software, manufacturing, robotics, and infrastructure teams.

Develop scalable backend services and APIs that integrate generative AI capabilities, LLMs, and machine learning models. Build microservices, data flows, caching, and secure integrations while ensuring reliability through testing, monitoring, observability, and performance optimization. Collaborate with product, design, data science, and ML engineering teams in an agile environment. Mentor junior engineers, conduct code reviews, improve CI/CD practices, and contribute to evolving R&D initiatives.

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.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Lead the design, development, and deployment of agentic AI solutions for networking infrastructure, including RAG systems, network automation, predictive maintenance, anomaly detection, and intelligent network management. Architect scalable AI systems, establish governance and MLOps practices, collaborate with networking stakeholders, mentor AI engineers, troubleshoot complex issues, and guide strategic adoption of emerging AI technologies.

Own the quality strategy and end-to-end testing pipeline for an AI-generated product handling real-money transactions. Build automated UI, browser, mobile, pre-merge, nightly, release, and production testing gates; integrate testing with CI/CD; evaluate external tools; and improve reliability of critical financial flows. The role partners with engineering and DevOps, uses AI coding agents daily, and drives quality initiatives as an autonomous individual contributor.

Serves as a technical advisor to customers, leading cloud and AI solution strategy, presales engagements, architecture discussions, demonstrations, and proofs of concept. Partners with Microsoft account teams, engineering groups, and external partners to secure technical wins, address capability gaps, support project delivery, communicate competitive insights, and align Microsoft technologies with customer business, security, and transformation goals.

Leads technical sales engagements for strategic APAC professional services customers. Designs modern data platforms and AI-ready architectures using Microsoft Fabric, Azure Databricks, Azure databases, Purview, Azure AI, RAG, semantic search, and governed analytics. Influences enterprise architecture decisions, leads proofs of concept and workshops, resolves technical blockers, develops reusable industry solutions, and connects Microsoft Data + AI capabilities to secure production workflows and professional services outcomes.

Develop and deploy production-grade AI and machine learning solutions for industrial manufacturing. Own projects from problem definition through operational integration, conduct data-driven optimization, advance ML and LLM-based methods, and collaborate with external partners. Coach employees on AI, establish standards, and advise stakeholders. The role requires Python, Azure, Kubernetes, infrastructure as code, cloud AI architectures, process simulation, optimization, and experience scaling ML systems.

Leads Microsoft 365 Copilot adoption across government agencies by delivering training, developing AI agents with Agent Builder and Copilot Studio, enabling internal trainers, creating learning materials, and managing change programs. The role also measures adoption, advises senior stakeholders, and ensures compliance with government security, governance, and data-handling requirements.

Build and maintain end-to-end full-stack applications and cloud infrastructure incorporating AI and GenAI workloads. Integrate LLMs, RAG, AI agents, and agentic workflows into user-facing products. Design APIs, databases, CI/CD pipelines, containerized deployments, and infrastructure as code across major cloud platforms. Support secure, reliable, observable systems while serving as a technical contact for clients and operations teams and producing technical documentation.

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.