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 delivery of complex, scalable software using AI-assisted development. Mentor engineers, implement CI/CD and DevOps practices, establish AI/GenAI best practices and guardrails, validate AI-generated outputs, and contribute to reusable accelerators, presales, and Gen-E2 methodology.

Design, build, and maintain production-grade full‑stack applications using modern frameworks. Implement CI/CD and testing, ensure code quality and security, and use generative AI tools (e.g., Copilot) and prompt engineering as core parts of the development workflow. Validate AI outputs, collaborate with product teams, and contribute to internal Gen-E2 practices and playbooks.

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Lead and develop AIAP projects and technologies, train and mentor AI apprentices, manage recruitment and syllabus updates, and run workshops and community engagement while delivering end-to-end AI solutions.

Design and build secure integrations between a Digital Twin platform and enterprise systems (CMMS, BMS, SCADA, IoT). Implement REST APIs, automation pipelines, and Model Context Protocol tools to enable human-supervised AI agent workflows with audit trails, permissions, and safeguards. Collaborate with architects, product, and FM partners to translate requirements into scalable, secure integrations.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.

Build and maintain full-stack web applications using React/Vue frontends and Java/Spring Cloud microservices. Design and consume REST/AI model APIs, work with SQL and NoSQL databases, use AI-assisted development tools, own features end-to-end, collaborate across product and design, ensure high-quality UI/UX, write tests, and contribute to documentation and architecture.

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.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Lead development and deployment of agentic AI and RAG solutions for network automation, predictive maintenance, anomaly detection, and intelligent network management. Architect scalable network AI systems, drive AI strategy with networking teams, mentor engineers, implement MLOps/CI-CD, and ensure performance, governance, and regulatory compliance.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

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.

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

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, operate, and maintain hybrid AI platforms. Implement automation-first platform engineering, ensure reliability/scalability/observability, collaborate with architects and infra teams, and troubleshoot production issues.

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.