Top AI Engineer Jobs in Singapore

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

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.

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.

Design, deploy, and scale production AI systems and applications. Responsibilities include building data pipelines, tuning and hardening models, collaborating with researchers, engineers, and product managers, monitoring live models, tracking AI advances, conducting code reviews, and owning solutions end to end amid changing priorities.

Research and develop novel AI models, algorithms, and systems; validate them through rigorous experiments and large-scale distributed training; and transition prototypes into production. The role involves deep learning, LLM pre-training and alignment, efficient GPU/TPU utilization, scientific analysis, technical writing, mentoring, code and paper reviews, open-source contributions, and end-to-end project ownership.

Leads Microsoft 365 Copilot adoption across Singapore’s Ministry of Home Affairs agencies. Delivers basic and advanced Copilot, Agent Builder, and Copilot Studio training; develops AI agents; creates enablement materials; enables internal trainers; manages stakeholder engagement, change programs, adoption measurement, and governance within a secure government environment.

Intern will work with mentors to define, prototype, validate, and demonstrate AI solutions. Responsibilities include developing Generative AI and Agentic AI workflows, prompt engineering, RAG, data-driven prototyping, and enhancing AI frameworks using Python, Databricks, and AWS. The role also explores OCR, image recognition, and multimodal AI use cases while translating business needs into technical approaches.

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.

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 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 operate production-grade agentic AI solutions in a customer-embedded role. Develop full-stack systems and AI agents, manage Azure infrastructure, maintain CI/CD pipelines and container platforms, handle monitoring and security alerts, administer IAM/RBAC, perform backups, manage costs, and support Day 2 operations. The role requires direct customer collaboration, technical delivery ownership, secure government-environment work, and eligibility for Singapore Government security clearance.

Designs and deploys Microsoft 365 Copilot and custom AI agents using Microsoft Agent Builder and Copilot Studio. Delivers Copilot and agent-development training, supports enterprise adoption, advises clients, troubleshoots implementations, and creates enablement materials. The role requires integrating Microsoft 365 services and enterprise data while complying with strict security and government data-handling requirements. It is client-facing, on-site in Singapore, and includes work with secured government facilities.

Develop, train, evaluate, optimize, and deploy machine learning and deep learning algorithms for intelligent surveillance products. Responsibilities include data preprocessing, feature engineering, labeling coordination, model innovation, algorithm integration, research into emerging AI technologies, and documentation. The role covers audio and video algorithms such as detection, feature extraction, tracking, and recognition, with collaboration across technical teams.

Design, build, and deploy production AI applications, including retrieval pipelines, LLM orchestration, APIs, and user interfaces. Own the software lifecycle, integrate model and vector database technologies, evaluate performance, manage CI/CD and cloud operations, and contribute reusable platform components. Collaborate with business teams and AI product managers while documenting architecture, communicating risks, and improving applications after launch.

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 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.

Designs and deploys agentic AI, generative AI, RAG, machine learning, and data science solutions for global financial markets. Builds production-ready AI services, APIs, reusable frameworks, data pipelines, monitoring, and evaluation capabilities. Partners with stakeholders, product owners, data scientists, and engineers to translate business requirements into scalable, accurate, and cost-effective AI solutions.

Provides expert guidance on securing AI systems throughout the development lifecycle in a railway operational technology environment. Reviews security submissions against frameworks including MITRE ATLAS, OWASP ML Security Top 10, NIST AI RMF, and IEC/ISO standards. Develops AI security guidelines, assesses supply chain risks, supports incident response, monitors emerging threats, and advises engineering and OT teams on secure AI implementation.