Top AI Engineer Jobs in Singapore

Designs and maintains scalable data pipelines, platform infrastructure, and backend services supporting AI Agent systems. Builds monitoring, alerting, observability, and data-quality capabilities while integrating distributed systems, databases, and message queues. Collaborates with AI, product, DevOps, frontend, and QA teams, and supports existing services to ensure stability and continuity.

Design, build, and ship agentic AI applications that answer business questions over governed data. Implement RAG, NL-to-query, tool/function calling, and evaluation/guardrails. Partner with data teams on metrics, models, pipelines, and apply strong data governance and security for financial services.

Lead design and implementation of cloud DevOps strategy for global Data and AI infrastructure. Build observability and detection capabilities, enable self-service tooling for data teams, manage scalable, secure infrastructure, and align DevOps practices and standards across geographies.

Design, build, and operate AI agents that automate SRE, DevOps, and DBA workflows across global infrastructure. Develop safe agent architectures with planning, reasoning, tool execution, error recovery, evaluation, and human escalation. Integrate agents with Kubernetes, Terraform, cloud APIs, databases, monitoring platforms, and CI/CD systems. Collaborate with infrastructure teams to identify automation opportunities and shape Airwallex’s infrastructure AI strategy.

Design, build, evaluate, deploy, and monitor LLM-powered agentic systems for regulatory workflows. Develop LangGraph/LangChain agents, evaluation and observability pipelines, RAG and vector-search systems, and production LLMOps practices. Integrate AI services into Node and React applications while optimizing prompts for cost, latency, and accuracy. Collaborate with product, engineering, and regulatory stakeholders, and mentor colleagues on agent design and evaluation.

Support ML engineers operating production training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through automation, tooling, documentation, runbooks, and operational processes. The role is remote in the Philippines or Singapore and follows a Sunday-Wednesday APAC shift.

Build and maintain LLM-powered, agent-based tools that automate developer and quantitative researcher workflows. Collaborate with platform and infrastructure teams to deliver scalable solutions, applying prompt engineering, tool integration, access controls, and observability. Own projects from prototyping through deployment, rollout, and documentation while promoting strong engineering standards. The role requires Python, LLM API integration, AI application development, Linux, networking, and software engineering fundamentals.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Lead the design, prototyping, development, deployment, and integration of generative AI solutions, including LLMs and GANs. Provide hands-on technical leadership, guide software development practices, mentor engineers, collaborate with product and research teams, and explore emerging technologies such as multimodal AI, agent automation, and retrieval-augmented generation.

Design, implement, and operate advanced batch and gang scheduling systems for large-scale AI/HPC clusters. Build admission control, queueing, topology-aware placement, DRA and custom scheduler plugins, and GPU-sharing/multi-tenancy solutions. Integrate scheduling with bare-metal hardware and storage, resolve contention and deadlocks, and mentor engineers to maintain scalable, reliable orchestration stacks.

Design and scale high-cardinality telemetry for AI datacenters: integrate hardware exporters into Kubernetes, build eBPF diagnostics, create alerting/dashboard pipelines, enable multi-tenant billing metrics, define observability standards, and lead architecture reviews and mentoring.

Design, build, evaluate, and deploy AI systems for enterprise customers, taking solutions from use-case definition and architecture through production launch and scale. Develop prototypes, integrations, evaluation harnesses, and reusable tooling while resolving complex technical challenges involving reliability, latency, cost, safety, security, governance, and observability. Partner with customer and internal engineering teams to drive adoption, measurable business outcomes, and product improvements.

Develop and deploy applied AI/ML solutions for intelligent transportation, including computer vision, generative AI, agentic AI, data pipelines, and machine learning algorithms. Collaborate with technical and business stakeholders, iteratively refine products with users, evaluate emerging technologies, and establish engineering best practices. The role also involves cloud solution deployment, DevOps practices, testing, version control, and knowledge sharing.

Provides expert guidance on securing AI systems throughout the development lifecycle in a railway operational technology environment. Reviews security submissions against frameworks including MITRE ATLAS, OWASP ML Security Top 10, NIST AI RMF, and IEC/ISO standards. Develops AI security guidelines, assesses supply chain risks, supports incident response, monitors emerging threats, and advises engineering and OT teams on secure AI implementation.

Operate and monitor large-scale datacenter networks for AI workloads, lead incident response, troubleshoot GPU-cluster networking (jitter, NCCL throughput), manage tickets and change control, produce reports, and coordinate with R&D and customers.

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 and build scalable multi-agent AI systems that automate infrastructure and business workflows. Integrate agents with cloud services, APIs, Kubernetes, telemetry, and observability platforms to perform autonomous remediation, code generation, and system analysis. Develop agent memory and RAG systems, implement evaluation and security guardrails, and establish deployment and governance practices for agentic applications. Collaborate with infrastructure teams to integrate autonomous agents into resilient cloud platforms.

Design, deploy, and maintain CSI drivers and GPUDirect Storage integrations for high-performance file systems; implement NVMe caching and optimize IOPS/throughput/latency across containerized storage. Collaborate on RDMA/InfiniBand tuning, define Kubernetes storage policies and multi-tenancy isolation, implement monitoring/alerting, and mentor junior engineers.

Develop, train, evaluate, optimize, and deploy machine learning and deep learning algorithms for intelligent surveillance products. Responsibilities include data preprocessing, feature engineering, model research, algorithm innovation, model compression, inference optimization across hardware platforms, documentation, and collaboration on product integration. The role focuses on computer vision applications including detection, tracking, feature extraction, and recognition.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Develops scalable backend applications, microservices, APIs, and LLM-powered features for risk technology. Responsibilities include prompt and context engineering, RAG, AI agents, vector databases, evaluation harnesses, guardrails, SQL and ORM development, full-stack contributions, CI/CD, deployment automation, monitoring, and DevOps. The role collaborates with stakeholders to translate technical and AI requirements into reliable production systems.

Lead design and delivery of generative AI agents and architectures, build robust APIs and scalable systems, roadmap AI applications, collaborate cross-functionally, troubleshoot production issues, and drive internal efficiency and customer-facing AI solutions.

Lead end-to-end design and implementation of generative AI solutions and APIs to improve customer-facing and internal automation. Build scalable, high-concurrency backend systems, collaborate with cross-functional teams, set best practices, and mentor engineers while driving deployment and MLOps for production AI products.

Designs, investigates, and optimizes infrastructure for production AI workloads across GPU environments, on-premises datacenters, private infrastructure, and public clouds. Diagnoses Linux, GPU, container, networking, storage, and hardware issues; develops benchmarking, diagnostic, deployment, and validation tooling; improves inference performance, GPU utilization, reliability, and scalability; and translates technical findings into product capabilities in collaboration with engineering and product teams.

Develop and deploy production-grade AI and machine learning solutions, including agentic and multi-agent systems, scalable ML pipelines, and real-time inference services. Design microservices, APIs, event-driven architectures, and CI/CD processes for AI applications. Lead technical design, mentor engineers and data scientists, conduct code reviews, and collaborate with product and platform teams. Apply advanced techniques across NLP, computer vision, recommendation systems, and predictive analytics while improving model performance and reliability.