Top AI Engineer Jobs in Singapore

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.

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.

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.

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.

Build scalable batch and real-time ETL pipelines and trusted customer-data foundations for analytics, machine learning, operational decision-making, and AI applications. Develop data models and products across warehouses and cloud infrastructure, using technologies such as Databricks, dbt, Spark, Kafka, and Flink. Collaborate with globally distributed teams on complex technical work.

Build and own scalable batch and real-time ETL pipelines for Airwallex’s Customer Data Platform. Develop trusted data foundations, warehouses, models, and data products supporting analytics, machine learning, operational decisions, business intelligence, and AI applications. Partner with globally distributed teams to deliver complex technical solutions using modern data infrastructure and streaming technologies.

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.

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.

Build and support end-to-end AI applications for semiconductor manufacturing, including data pipelines, machine learning models, APIs, web interfaces, automation tools, and observability. Develop Python and JavaScript/TypeScript solutions, integrate LLMs and agentic AI, connect robotics and manufacturing systems, and deploy containerized applications using Kubernetes or OpenShift. Contribute to CI/CD, Responsible AI, model evaluation, monitoring, testing, documentation, and optimization.

Develop and maintain enterprise AI platforms and shared services, including reusable frameworks, integrations, APIs, and AI capabilities. Partner with delivery, innovation, engineering, architecture, and business teams to accelerate AI solution deployment. Support platform reliability, scalability, security, governance, privacy, responsible AI, troubleshooting, documentation, and operational excellence. Evaluate emerging technologies such as LLMs, AI agents, MCP, RAG, and cloud AI services while contributing to platform roadmaps and technical best practices.

Develops and supports software and AI-enabled IT solutions for semiconductor manufacturing. Responsibilities include requirements analysis, coding, testing, CI/CD, application lifecycle management, global releases, UAT, monitoring, maintenance, system recovery, and L3 operational support. The role requires collaboration with users and engineering teams, occasional cleanroom work, and participation in 24/7 support rotation while applying approved AI tools securely and responsibly.

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.

Design, deploy, and optimize agentic AI workflows and integrations for enterprise customers. Lead implementations from scoping through adoption, connect agents to customer data and systems, establish prompts, evaluations, guardrails, and human oversight, and coordinate engineering, data science, product, and customer success teams. Serve as the primary technical customer partner while managing roadmaps, requirements, delivery risks, and measurable implementation outcomes.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Designs and delivers customer-facing technical engagements, including strategy sessions, architecture workshops, prototypes, and AI transformation initiatives. Leads discovery, solution architecture, technology selection, and cross-platform cloud integration. Advises customers and internal sales teams on Azure AI capabilities, security, scalability, and performance while coordinating cross-functional stakeholders and shaping strategic account plans.

Supports LaserJet hardware qualification and validation through test automation, engineering data analytics, and AI-enabled workflows. Develops scripts, dashboards, and tools for test execution, data collection, reporting, troubleshooting, and anomaly detection. Prototypes generative AI solutions and agents, collaborates with hardware and test engineers, and documents and validates tools for broader adoption.

Design and build production-ready enterprise AI architecture blueprints and open-source Quickstarts, integrate and benchmark model serving and telemetry, ensure secure, compliant deployments, and co-develop integrations with partners.

Lead design, implementation, and productionization of enterprise AI solutions on Red Hat AI Platform and OpenShift across APAC. Deliver end-to-end AI workloads (data pipelines, training, LLM serving, inference), drive pilot-to-production, implement responsible AI controls, mentor team members, and produce architecture and operational documentation.

Build and deploy AI/ML solutions for APAC local rates Sales & Trading, focusing on pricing optimization, bid-offer calibration, risk management, dashboards, alerts, and generative AI applications. Develop data pipelines, APIs, and production tooling using market, trade, and client data. Partner directly with traders, sales professionals, quantitative researchers, compliance, and risk teams to deliver commercially impactful solutions in a fast-paced trading-floor environment.

Lead production AI initiatives including LLM agents, AI drafts, and a shared Context Engine. Own architecture decisions, evaluation systems, guardrails, observability, reliability, and quality measurement. Deliver major AI projects from design through production while providing cross-functional technical leadership and improving engineering standards. Build backend and platform infrastructure using Python, Django, PostgreSQL, Redis, asynchronous workers, and REST or GraphQL APIs.

Lead enterprise technical sales engagements focused on developer productivity, AI-assisted development, and Agentic SDLC adoption. Design secure, scalable architectures, conduct proofs of concept, hackathons, code-with sessions, and architecture workshops, and guide customers through DevOps, cloud-native development, and AI platform adoption. Partner with developers, engineering leaders, and Microsoft product teams to resolve technical blockers, shape solutions, and connect productivity, quality, security, and delivery improvements to business outcomes.

Lead pre-sales technical execution for regional AI security solutions. Partner with sales teams, train sales engineers and channel partners, conduct technical discovery, architecture reviews, demonstrations, proofs of concept, deployments, and customer training. Manage demo environments and benchmarking, support post-sales handoffs, and build relationships with technical decision-makers. Serve as an AI security evangelist while helping customers secure AI applications, infrastructure, and agentic AI deployments.

Serves as a technical advisor to Singapore Government agencies, designing secure cloud, data, analytics, and AI architectures using Microsoft technologies. Leads executive workshops, architecture reviews, demonstrations, proof-of-concepts, and responsible AI transformation initiatives. Partners with sales, engineering, and customers to shape technical strategies, accelerate adoption, influence decisions, and deliver business outcomes. Also contributes to practice development, reusable assets, technical readiness, and peer mentoring.

Build and operate production backend services supporting Supabase AI products, including the MCP server. Responsibilities include service architecture, deployments, scaling, authentication and authorization, observability, reliability targets, on-call incident response, rate limiting, capacity management, and API evolution. The role requires strong TypeScript or Go skills, production backend experience, distributed-systems debugging, and collaboration with Platform and SRE teams in a fully remote environment.

The AI and Robotics Engineer will support AI and robotics standards development across Asia, conduct applied research and system testing, develop technical methods and reports, and deliver training and certification programs. The role engages research institutes, industry, government agencies, and standards bodies; contributes to committees and events; tracks regional developments; and translates findings into standards, briefings, and presentations. The position requires hands-on engineering experience with robotics or applied AI, strong technical communication, and approximately 15–20% travel.