Top AI Engineer Jobs in Singapore

The AI Engineer will solve complex problems using AI engineering expertise, maintain client relationships, and continuously improve skills in AI and data.

The Lead AI Engineer will solve complex AI challenges, mentor a team, and develop client relationships while delivering technical thought leadership.

Lead QA for AI-powered SaaS features: design and execute functional, API, integration, regression, exploratory, and end-to-end tests. Validate AI outputs, investigate root causes across frontend/backend/APIs/databases, build automation, improve CI/CD quality gates, and collaborate with engineers, product, and security teams to raise release confidence.

As an AI Engineer at SimplifyNext, you will develop machine learning models, integrate AI solutions, perform data engineering tasks, and collaborate with SMEs to deliver impactful AI-driven solutions.

As an AI Engineer, you will build and deploy AI solutions, work with data preparation, integrate models into applications, and collaborate with teams to solve business problems using AI.

Design, build and maintain scalable ETL/ELT data pipelines and integrations on Microsoft Fabric and Databricks. Prepare and model data for AI/ML and analytics, ensure data quality, lineage, security and governance, develop connectors/APIs, support CI/CD deployments, monitor pipelines, and document handovers while collaborating with stakeholders and AI/ML engineers.

Develop and maintain scalable backend services and APIs, collaborate with frontend developers, and lead new features and infrastructure improvements.

Work with R&D to design, develop, and maintain AI applications and RPA solutions. Build AI/software applications and APIs, identify automation opportunities, integrate RPA bots with APIs, databases and third-party systems, and perform testing, debugging, and performance optimization.

Work with the R&D innovation team to design, implement, and optimize computer vision and perception algorithms. Develop and test object detection, tracking, and recognition models, conduct literature reviews and benchmarking, experiment with novel approaches, train and evaluate models on large datasets, and collaborate to integrate vision algorithms into production embedded systems.

Build and ship production AI systems: agents, RAG/LLM apps, tool-calling and automations for onboarding, KYC, risk, support and operations. Work with product and engineering, design for accuracy, latency, cost, security and compliance, and convert manual processes into scalable AI-enabled workflows.

Develop and implement AI solutions for game development, enhance integration with game engines, and collaborate with teams to analyze player engagement.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

As an AI Engineer, you'll build prototypes, design system architectures, write code, integrate AI services, and manage deployments, ensuring software solutions meet public sector needs.

The AI Engineer builds prototypes, designs system architectures, writes production-quality code, manages deployments, and collaborates on AI platform services for government applications.

The Research Engineer will conduct research, develop algorithms, design experiments, translate concepts into prototypes, and analyze data, focusing on sustainable digital solutions.

The AI Engineer will lead AI projects, train apprentices, develop AI technologies, manage recruitment, and support industry activities.

The intern will develop web applications and dashboards, manage clinical trial data infrastructure, utilize AI tools, and enhance data visualization for decision-making in drug development.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Design and deliver production-grade, enterprise-focused AI architecture blueprints and open-source Quickstarts. Collaborate with partners to integrate and benchmark systems, incorporate telemetry, ensure secure and compliant deployments, and optimize runtimes across heterogeneous hardware.

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.

Design, train, evaluate and deploy machine learning and AI models. Build data pipelines, preprocess data, run EDA, optimise algorithms, deploy models to production (cloud/APIs/containers), monitor performance, document models, and collaborate with cross-functional teams on AI prototypes and production solutions.

As a Principal AI Engineer, you will lead the architecture and deployment of large-scale conversational chatbot systems, optimizing their performance for enterprise and consumer use cases.

Lead design, development, testing, and App Store submission of production-scale iOS features in Swift. Collaborate cross-functionally, enforce code quality via reviews and refactoring, maintain technical documentation, and adopt AI-assisted tooling to improve productivity across large, user-facing modules.