Top AI Engineer Jobs in Singapore

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.

Develop and validate computer vision models and workflows for soil photography and XCT/CT laboratory imaging. Tasks include image classification, detection, segmentation, sample quality assessment, dataset preparation/annotation, integration with lab/LIMS and geotechnical data, and documentation of methods and validation against expert ground truth.

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.

Design, build, and deploy end-to-end agentic AI and LLM applications for enterprise customers. Responsibilities include model development (RAG, fine-tuning), production ML pipelines, Kubernetes/Helm deployments (cloud, on-prem, air-gapped), LLMOps and monitoring, customer-facing technical delivery, troubleshooting, workshops, and ensuring secure, production-grade AI with guardrails and performance sizing.

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.

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.

Design, develop, and deploy enterprise Generative and Agentic AI applications using LLMs. Architect cloud-native AI solutions, build PoCs, support pre-sales, run client workshops, and provide technical advisory across the solution lifecycle.

Design and build generative and agentic AI solutions and end-to-end ML applications. Analyze structured and unstructured data, develop ML models, deploy pipelines, dashboards and cloud architectures. Advise clients, run workshops, support pre-sales PoCs, and translate business requirements into scalable AI solutions while collaborating with stakeholders.

Develop and iterate full-stack AI products for the life sciences domain, optimize data processing and business logic for pharmaceutical accuracy, and rapidly adopt and integrate frontier LLMs, APIs, and AI tools.

Develop advanced calibration, localization, and mapping algorithms for autonomous robots, ensuring high-quality code delivery and collaboration across teams.

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.

Design, build, and deploy production AI/ML and Generative AI solutions (LLM apps, RAG, agentic workflows). Build data pipelines, MLOps/LLMOps, and cloud-deployed architectures (AWS/Azure/GCP). Collaborate with stakeholders, embed responsible AI and governance, and create reusable assets and best practices to scale AI across the organization.

Design, build, and maintain internal platform products, APIs, and services that enable customer-facing teams to prototype and scale solutions. Own full product lifecycle from prototype to GA, collaborate with customers and field teams, leverage AI-assisted development, and deliver secure, scalable systems processing large volumes of security events.

Design and implement scalable LLM-powered applications, microservices, and APIs. Build AI agents, RAG-enabled semantic search, vector DB integration, CI/CD pipelines, and guardrails. Collaborate with stakeholders, own backend and full-stack development, and support DevOps, testing, and production reliability.

The Director, AI Solutions Engineer leads AI integration in marketing, develops APIs and UIs, ensures compliance and security, and provides technical support for AI models.

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.