Top AI Engineer Jobs in Singapore

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.

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 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.

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.

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.

Senior software engineer builds, deploys, and supports IT applications for semiconductor manufacturing. Owns full application lifecycle, uses AI-assisted tools, implements CI/CD, monitors system performance, coordinates worldwide releases, provides L3 24/7 support, and works in clean-room environments to improve productivity and reliability.

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

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 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.

Build scalable batch and real-time ETL pipelines, data models, and data products for Airwallex’s Customer Data Platform. Develop trusted data foundations across warehouses and cloud infrastructure to support analytics, machine learning, operational decision-making, business intelligence, and AI applications. Partner with globally distributed teams on complex technical work and apply data engineering best practices.

Build scalable batch and streaming ETL pipelines for Airwallex’s Customer Data Platform. Design data foundations, schemas, and models across databases and warehouses to support business intelligence, machine learning, operational decisions, and AI applications. Partner with globally distributed teams on complex technical projects while applying strong problem-solving and collaboration skills.

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 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.

Leads technical pre-sales engagements for Microsoft AI workforce solutions, including demos, pilots, architecture design, adoption planning, and technical decision-making. Partners with sales, customer success, engineering, and partners to generate pipeline, remove blockers, secure competitive wins, and support Copilot and Agents adoption. Builds secure, scalable architectures, manages deal hygiene, develops executive relationships, and provides feedback to product and sales leadership.

Build and operate AI-assisted workflows and system integrations for research administration. Responsibilities include developing REST and GraphQL connectors, agent tooling with deterministic guardrails, LLM-based intake and classification, milestone monitoring, testing, documentation, user acceptance, and presenting shipped solutions to leadership.

Execute automated LLM safety testing, red teaming, adversarial testing, and architecture reviews for agentic, multimodal, and RAG systems. Document vulnerabilities, recommend remediations, support compliance with AI safety standards, mentor AIAP apprentices, and contribute to responsible AI community activities through workshops, talks, and documentation.

Design, train, and deploy reinforcement and imitation learning policies for humanoid robots. Run physical robot experiments, improve sim-to-real transfer, maintain simulation environments and data pipelines, analyze deployment failures, and collaborate with hardware and firmware teams to improve policy robustness.

Build and operate production-grade agentic and generative AI solutions for clients. Responsibilities span solution design, model selection, RAG, deployment, MLOps, monitoring, evaluation, observability, security, governance, incident response, resilience, cost optimization, and auditability. The role partners with data, engineering, security, and client stakeholders, establishes reusable production standards, supports regulated environments, mentors junior engineers, and contributes to the growth of CFGI’s Asia-Pacific Data & AI practice.

Design, build, and deploy identity-risk, fraud-detection, behavioral, graph, biometric, and multimodal AI models for real-time financial-platform defenses. Develop graph and clustering methods to identify coordinated fraud, engineer behavioral features and sequence models, integrate verification and device signals, and manage production monitoring, drift, latency, and fraud metrics. Collaborate with product, risk, compliance, infrastructure, and engineering teams while advancing applied AI research and mentoring junior engineers.

Build and deploy production-grade generative AI systems for fintech, including stateful agents, multimodal RAG pipelines, model evaluation, red teaming, security guardrails, observability, and cloud infrastructure. The role requires deployment autonomy across AWS or GCP, containerization, CI/CD, retrieval engineering, and secure AI governance for financial data and advice.

Design and deploy production-ready agentic AI systems for public-sector fraud detection. Build LLM workflows for emerging risk identification and detection logic, integrate AWS and enterprise data platforms, and develop tested Python applications and APIs. Establish evaluation, monitoring, CI/CD, observability, governance, security, human oversight, and responsible-AI safeguards while collaborating with data scientists, analysts, engineers, and business stakeholders.

Conduct end-to-end adversarial testing of LLM-powered systems, including jailbreaks, prompt injection, data leakage, tool misuse, and policy bypasses. Automate attacks with Python, build reusable attack libraries and regression tests, document reproducible security findings, and translate model failures into product requirements and customer-facing evidence. Support demos, security reviews, and sales conversations while tracking emerging AI security techniques.

Build and own Supabase’s internal AI execution platform, including event-driven agent runtimes, durable state, human review, rollback, observability, evaluations, governance, and least-privilege tooling. Develop production agents, MCP integrations, infrastructure on Python and GCP, CI evaluation gates, operating metrics, and return-on-investment instrumentation. As the sole platform engineer, independently make architecture decisions, operate systems end to end, and ensure unsafe actions are structurally impossible.