Top AI Engineer Jobs in Singapore

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.

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.

Develop, train, evaluate, optimize, and deploy machine learning and deep learning algorithms, especially computer vision models for surveillance and monitoring. Responsibilities include data preprocessing, feature engineering, labeling coordination, model research, target detection, tracking, recognition, platform optimization, documentation, and cross-functional collaboration.

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.

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.

Designs and validates data center network architectures for AI infrastructure, including BGP, EVPN/VXLAN, RoCEv2, QoS, and RDMA fabrics. Builds qualification labs, tests switches, NICs, and network operating systems, investigates performance and interoperability issues, troubleshoots congestion and packet loss, and develops documented standards, automation, and product feedback for customer deployments.

Develops AI-enabled applications for digital air traffic control assistants by integrating LLMs, RAG, AI agents, APIs, databases, enterprise systems, and operational data. Builds backend services, user interfaces, connectors, evaluation and monitoring systems, and end-to-end prototypes. Collaborates with domain experts to define requirements and tests, and supports demonstrations, documentation, deployments, technical reports, publications, and industry or regulatory partnerships.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

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.

Build AI and agent-based solutions, ML models, full-stack React and Python applications, and production-grade AI platform services. Develop MLOps and CI/CD pipelines across GCP and on-premises Kubernetes, deploy model-serving infrastructure, implement observability and security controls, and create reliable agent guardrails, evaluations, and audit trails. Collaborate with enterprise SMEs, contribute to architecture standards, communicate technical recommendations, and own solutions from design through support.

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.