Top AI Engineer Jobs in Singapore

As an AI Engineer at Amity AI Labs, you'll develop PoC-grade AI solutions, turn research into practical implementations, and collaborate with subsidiary teams on AI/ML projects.

The Senior .Net Developer will enhance SDG's C#/.NET Core codebase, design high-load services, and optimize MS SQL queries, benefiting from a remote work environment and attractive benefits.

Lead design, development, and deployment of scalable AI/ML applications and microservices on Kubernetes. Build end-to-end MLOps pipelines, mentor AI engineers, evaluate emerging LLM and GenAI tools, ensure observability and governance, and produce technical documentation and runbooks.

Build end-to-end software features across frontend, backend, and AI components. Responsibilities include developing interfaces, APIs, backend services, data pipelines, automation workflows, and LLM-powered applications such as RAG systems and AI agents. The role involves testing, documentation, debugging, cross-functional collaboration, product iteration, and using AI coding tools to improve development speed and quality.

Develop and deploy end-to-end AI solutions for industry partners, from scoping and feasibility assessment through maintenance. Mentor AI apprentices through technical coaching, project delivery, and recruitment activities. Apply machine learning, generative AI, LLMs, software engineering, MLOps, and cloud technologies. Collaborate with external organizations to define requirements and deliverables, while supporting AI Singapore’s outreach, talent development, and ecosystem-building initiatives.

As an AI Application Engineer, you will debug and improve platforms, develop AI tools and proof-of-concepts, and collaborate across teams to enhance AI systems.

Design, build, deploy and govern AI solutions (LLMs, agentic systems, MLOps). Collaborate with clients and partners, deliver end-to-end AI projects, and continuously upskill in AI engineering and deployment best practices.

Lead engineering for an AI-native Web3 product: ship production features, turn AI-assisted prototypes into scalable systems, own architecture/security/quality, direct AI coding agents, define engineering standards, and collaborate with founders on product and technical strategy.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Build and maintain full-stack software and production AI applications for government product teams. Develop LLM workflows, evaluation pipelines, observability, guardrails, testing, and deployment processes. Collaborate with customers on discovery, demos, proofs of concept, architecture, onboarding, integrations, support, and troubleshooting. Maintain technical documentation and improve accessibility, including remediating WCAG issues and learning assistive technologies.

Build and operate governed, audit-ready data foundations for client analytics and AI solutions. Responsibilities include designing ingestion pipelines, lakehouse and warehouse architectures, transformation and semantic layers, data quality, master data, lineage, privacy, residency controls, and AI-ready datasets. The role partners with AI engineers, data scientists, and client stakeholders, contributes reusable engineering accelerators, owns engagement-scale data architecture, and mentors junior resources. It is a hybrid Singapore-based Manager-level position with occasional APAC travel.

Build and operate production backend systems powering AI features. Design inference pipelines, orchestration layers, and model service boundaries while optimizing latency, throughput, caching, batching, and streaming. Monitor reliability through logging, alerting, and incident response, and develop stable APIs for mobile, desktop, frontend, and machine learning integrations. Debug distributed systems under load and continuously improve performance based on production behavior.

Build and ship production-grade AI product features across frontend, backend, and AI integrations. Design reliable multi-step agent workflows with planning, tool use, memory, streaming, failure recovery, and fallback mechanisms. Integrate LLMs, RAG systems, and external tools while improving latency, observability, monitoring, and workflow success rates. Collaborate with ML, backend, and product teams to deliver scalable AI-native experiences.

Provide deep technical support for customers using AI developer products by reproducing, diagnosing, and resolving environment-specific issues across codebases, cloud infrastructure, CI/CD, containers, APIs, and developer tooling. Perform root-cause analysis, communicate findings, escalate with full context, create playbooks and automations, and support release testing to improve reliability and adoption.

Design, build and operate enterprise-scale data and analytics platforms on GCP. Lead architecture, reliability and incident response for BigQuery/Dataproc/Spark workloads, implement IaC and CI/CD, drive SRE/observability, optimize performance and cost, mentor engineers and enforce security and operational best practices.

Design, prototype, and deploy AI-driven telecom solutions (Generative AI, LLMs, Agentic AI, digital twins). Build data pipelines, fine-tune models, implement MLOps, integrate hardware and cloud systems, and collaborate with telecom experts to deliver scalable 6G/5G network optimization and automation solutions.

Assist in building and maintaining data ingestion pipelines and storage for AI workflows. Implement deployment, security, and monitoring for applications; collaborate with stakeholders and design teams; test and validate data verification and AI workflows; document solutions and follow best practices while continuously learning new tools.

Design, build, and deploy agentic AI solutions and co-pilots: architect end-to-end AI pipelines, implement LLM- and RAG-based agent workflows, integrate with enterprise systems, prototype POCs, and ensure performance, reliability, governance, and stakeholder collaboration.

Operate, maintain, and optimize hybrid cloud Data and AI platforms (Cloudera CDP/CML, Hadoop, Spark) across AWS and on-prem. Perform platform upgrades, security patching, container/Kubernetes/OpenShift administration, monitoring, troubleshooting, and automation using Shell/Python. Collaborate with Infrastructure, Security, SRE, and Data/ML teams to ensure availability and scalability of AI/ML and Generative AI services (SageMaker, EMR, Bedrock).

Lead and operate enterprise Azure and Microsoft 365 platforms, manage hybrid identity (Entra ID/AD), deliver and govern AI solutions (Azure OpenAI, Azure ML, Copilot), provide L2/3 support, secure and govern cloud resources, manage on‑prem infrastructure, and drive vendor, project, and stakeholder engagement to scale AI initiatives.

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.

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.

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.