Top AI Engineer Jobs in Singapore

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 innovative AI web applications and APIs with the R&D innovation team. Integrate frontier large language models into automotive software, design scalable and usable applications, and perform testing, debugging, and performance optimization. The role also involves applying knowledge of robotics, sensors, actuators, and perception technologies, with opportunities to use ROS/ROS2, Docker, humanoid robotics, and navigation algorithms.

Design, develop, and deploy enterprise generative AI agents and full-stack AI applications. Translate client business needs into prototypes, production solutions, and implementation strategies. Build agent workflows using RAG, tool calling, memory, orchestration, guardrails, and human-in-the-loop processes. Integrate applications with enterprise systems, APIs, databases, and cloud platforms. Support consulting workshops, technical advisory, pre-sales demonstrations, and production monitoring while communicating effectively with English- and Japanese-speaking stakeholders.

Partner with startup customers to ideate, prototype, deploy, and scale generative AI applications using the OpenAI API. Provide technical guidance, identify business opportunities, collaborate with Sales, Product, and Research teams, contribute to open-source resources, share best practices, and deliver customer feedback internally. The role requires strong software or machine learning engineering experience, startup aptitude, Mandarin fluency, and proficiency in Python, JavaScript, and AI/LLM development.

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.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop efficient models and pipelines, improve search relevance and personalization, fine-tune existing models, monitor production performance, and optimize accuracy, latency, cost, and reliability. Collaborate with engineering and product teams to ship measurable improvements for AI-native applications.

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.

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.

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.

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 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.

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.

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.

Serve as the technical expert for OpenAI Codex customers, designing and scaling AI-enhanced software development workflows. Build demos, integrations, automations, and reference implementations; lead technical workshops; contribute developer guidance; gather product feedback; and advise engineering organizations on architecture, security, operational readiness, and AI adoption strategy. The role requires Mandarin proficiency, substantial customer-facing technical experience, and strong expertise in AI coding tools and developer productivity.