Top AI Engineer Jobs in Singapore

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Partner with platform teams and consumers, contribute tooling and standards, and lead delivery for workstreams while supporting junior engineers.

Design, develop, and deploy AI solutions by integrating ML models, optimizing workflows, and collaborating across teams. Ensure performance and security in production environments.

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.

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.

Early-career DevOps role focusing on Kubernetes and AWS operations, embedding security into the development lifecycle, automating processes, and implementing CI/CD solutions. Remote, fixed-term accelerator program for students and recent graduates.

Build and optimize high-traffic, responsive web applications and hybrid in-app pages. Implement user-facing features, improve performance and scalability, and create libraries/frameworks to accelerate development and enhance code quality.

Design, build, and maintain high-performance backend services and APIs using Java/Spring Boot. Develop and productionize recommendation algorithms and LLM systems, optimize LLM workloads (including on-prem), ensure scalability, reliability, and security, and collaborate across time zones with product and project teams to own end-to-end architecture.

Responsible for functional and API testing of platform products, designing and executing end-to-end tests across frontend, backend, strategy, and algorithms. Validate personalized user experiences, improve test coverage and QA processes, and drive adoption of AI-assisted test generation and automation. Collaborate with Product, Engineering, Algorithm, and Strategy teams to identify issues and ensure quality.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Early-career full-time program offering training, mentorship, and hands-on work within Binance's blockchain ecosystem. Participants develop practical skills in Web3, collaborate across teams, and contribute to product and engineering projects while receiving tailored curriculum and 1:1 mentorship.

Design and implement secure integrations between Digital Twins and enterprise systems (CMMS, BMS, SCADA, IoT, CCTV). Build MCP tools, REST APIs, and automation pipelines enabling AI agents to query, trigger operations, and dispatch tasks. Ensure human-in-the-loop controls, audit trails, permissions, and security. Collaborate with architects, product, and FM partners to translate business needs into scalable integration solutions.

Operate and harden Kubernetes-based AI/MaaS production environments; define SLOs, alerting, dashboards, and runbooks; improve rollout safety with canaries and fast rollbacks; drive GPU capacity planning; automate operations with Helm, Argo CD, operators and scripts; and partner to debug incidents from API edge to model workers.

Design, build, deploy and maintain scalable AI/ML applications, LLM services and data pipelines. Collaborate with data scientists and product engineers, ensure performant, reliable, and well-tested code, influence roadmap, and provide technical leadership and mentoring.

Design, build, and deploy AI-enabled products (RAG, retrieval, reasoning) and supporting web systems and data pipelines. Integrate emerging AI technologies, prototype rapidly, collaborate with cross-functional teams, and ensure robust, scalable, and secure software solutions that meet analyst needs.

Design, build and deploy scalable full-stack web features, focusing on an internal AI-powered chat assistant. Ensure high-quality, well-tested code, maintain production reliability, participate in code reviews, collaborate with designers and stakeholders, and contribute to documentation and Agile delivery.

Develop and maintain scalable backend systems, implement APIs, manage databases, and collaborate with stakeholders to deliver AI solutions.

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.

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.

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.

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.

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.

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.

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

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.