Top AI Engineer Jobs in Singapore

Provide high-touch technical support for OpenAI products, troubleshoot complex issues, automate tooling with scripting/AI, partner with engineering and product to drive improvements, and support on-call coverage.

Early-career full stack internship developing frontend and backend features under senior guidance. Integrate AI coding tools into workflows, translate product requirements to implementations, contribute across the product lifecycle, and suggest cross-team process improvements while learning full-stack practices.

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.

Design, evaluate, and deliver AI solutions for gaming. Build scalable AI services, mentor teams, and communicate technical concepts effectively.

Work on prototyping and researching GenAI solutions (RAG, agentic AI, fine-tuning). Build PoCs, develop tools aligned to business use cases, experiment with open-source AI stacks and industrial AI services, and support prototype industrialization under supervisor mentorship.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

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.

Early-career TechOps role: build dashboards/alerts, script repetitive ticket steps, participate in RCAs, log user pain points and propose fixes, create a lightweight agent (e.g., Telegram bot) with reusable skills, optimize incident-management workflows, and write actionable runbooks to improve support-driven user experience.

Design, develop, and deploy agentic AI solutions to optimize network automation, predictive maintenance, anomaly detection, and intelligent network management. Collaborate with networking stakeholders to translate goals into technical requirements, ensure compliance with performance, risk, and regulatory standards, research emerging agentic AI trends, and provide troubleshooting, QA, and documentation for production AI systems.

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.

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 operate secure backend services that enable AI/ML and generative-AI solutions for government systems. Implement model pipelines, prompt workflows, RAG solutions, APIs, integrations, evaluation and monitoring; support responsible AI controls, security compliance, deployment in approved cloud environments, troubleshooting, and operational support across development and production stages.

Design and build internal AI productivity tools and LLM-powered applications to augment developer workflows. Integrate AI into the software development lifecycle, apply techniques like prompt engineering, RAG, and agents, partner with engineering teams to identify bottlenecks, and continuously evaluate emerging AI technologies to improve internal platforms and engineering efficiency.

Lead technical design and delivery of core products across backend, frontend, APIs, databases and infrastructure. Hands-on coding, system design, code reviews, production debugging, mentorship, and driving engineering initiatives. Ensure reliability, observability, security, and scalable architectures while collaborating with product, design and AI teams to ship high-quality features.

Lead technical design and execution of major products and systems, write and review code, make architecture decisions, mentor engineers, drive initiatives from design to production, own reliability, testing, security and observability, and collaborate with product, design and AI teams to deliver scalable, maintainable systems.

Technical lead responsible for designing and shipping scalable backend and frontend systems, making architecture decisions, writing and reviewing code, mentoring engineers, improving reliability, tooling and observability, and owning production incidents from diagnosis to resolution while collaborating with product, design and AI teams.

Lead technical design and execution of a scalable AI-native email product. Hands-on coding, system design across backend/frontend/APIs/databases/cloud, code review, production debugging, mentoring engineers, improving tooling, and owning reliability from design through incidents.

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.

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

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.

Design and build AI-powered developer platforms, agent orchestration, RAG/retrieval systems, and CI/CD-integrated workflows to automate code generation, testing, deployment, and engineering productivity. Drive AI adoption, define best practices, and measure impact across Group Wealth Engineering.

Design, develop, and evaluate AI-driven solutions for sustainable living. Collect and preprocess multimodal datasets, build data pipelines and experiments, analyze model performance, iterate on models, and collaborate on publications, reports, and demonstrations.

Internship role to design and build LLM-powered recommendation and agentic AI systems for financial and Web3 scenarios. Tasks include candidate generation, ranking, tool routing, post-training (SFT/RL), datasets and evaluation pipelines, prototyping agent workflows, and integrating prototypes into production with cross-functional teams.