Top AI Engineer Jobs in Singapore

Develop, train, fine-tune and evaluate multilingual and multimodal LLMs; build scalable multilingual text data pipelines; support large-scale distributed training, experiment tracking and compute optimisation; reproduce and translate research into production improvements; create reusable training/evaluation assets and deployment-ready components; collaborate with academic and industry partners and support community outreach.

Build and maintain end-to-end modules for a multimodal data platform, from frontend UI/UX to backend services and cloud deployment. Implement tooling for evaluations, benchmarks, and data annotation; integrate evaluation frameworks; set up CI/CD, monitoring, and cost-effective cloud infrastructure; collaborate with cross-functional teams.

Operate and maintain the Data Platform across GCP and AWS, ensuring reliability, security, cost and performance. Manage IaC and CI/CD, run incident response and post-mortems, operate central data management (pipelines, storage, catalogues, access controls), improve observability and automation, and apply AI tools to streamline ops and tooling.

Lead design and build of high-performance distributed data infrastructure and AI enablement platforms. Optimize systems internals and JVM performance, implement lakehouse and real-time streaming, develop scalable vector indexing and AI knowledge platform, own SDLC for petabyte-scale systems across global regions, drive engineering best practices, and mentor mid-level engineers.

Lead design, build, and operate distributed, large-scale (batch/streaming) data platforms and pipelines. Drive technical decisions, performance optimization, data architecture (lakehouse), tooling standards, third-party integration, and mentor junior engineers to enable company-wide data-driven capabilities.

The role involves pioneering machine learning techniques, developing advanced AI models, building data pipelines, and translating prototypes into products in code analysis.

The Senior AI Engineer will develop and deploy generative AI solutions in healthcare, focusing on system integration, deep learning algorithms, and large language models.

As a Senior AI Engineer, you will utilize AI engineering and delivery expertise to drive transformative projects for clients while building relationships and expanding your knowledge.

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.

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.

Build and operate DV's model layer: fine-tune and distill open-weight models, run on-prem inference infrastructure on Kubernetes, implement a model gateway routing across providers, evaluate model quality/cost/latency, manage GPU provisioning and utilization, and define tooling for model selection and production regression testing.

Design, build, and productionize GenAI and ML solutions for semiconductor engineering: large-scale data pipelines, LLM/RAG and agentic workflows, model training/inference, analytics, deployment, monitoring, and cross-functional collaboration to improve quality, cost, and engineering productivity.

The AI Solutions Architect will lead AI strategy, design AI architectures, assess AI opportunities, build AI solutions, and ensure compliance with governance frameworks.

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.

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

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.

Build and ship production-grade AI agents, LLM apps, RAG systems and automation workflows for a fintech neobank. Integrate retrieval, tool calling, evaluations and guardrails to improve onboarding, KYC, risk review, support, CRM and operational processes while meeting constraints of accuracy, latency, cost, security and compliance.

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.

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.

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

As a Fullstack AI Engineer, you will design and maintain AI-powered applications, develop Proof-of-Concepts, enhance AI frameworks, and collaborate on production-grade systems in a cloud-native environment.