Top AWS Jobs in Singapore

Build and operate AI agents, assistants, knowledge-base pipelines, APIs, integrations, and user-facing tools for company-wide AI adoption. Own features from design through production, including testing, CI/CD, observability, security guardrails, responsible AI, metrics, documentation, incident response, and continuous improvement based on internal user feedback.

Forward Deployed Engineer responsible for embedding with strategic clients, understanding mission-critical challenges, and designing and deploying production-grade technical solutions. Duties include building Python applications, SQL queries, APIs, data pipelines, AI/ML prototypes, and cloud integrations; leading technical workshops and deployments; advising senior stakeholders; debugging systems; and translating client feedback into scalable product improvements.

Design and operate enterprise AI platforms for a banking group, including multi-agent frameworks, LLM inference infrastructure, MLOps pipelines, and hybrid AWS/on-premises deployments. Build production AI services, optimize GPU utilization and inference latency, manage model lifecycles, establish CI/CD and observability, and provide daily platform operations. The role requires strong Python, cloud, AI framework, inference-stack, Kubernetes, infrastructure-as-code, and DevOps expertise, with AVP and VP experience levels ranging from 4+ to 8+ years.

Build and maintain internal developer platform software, APIs, CLIs, infrastructure modules, developer portals, and secure remote development environments. Implement AWS, Kubernetes, GitOps, CI/CD, observability, security, reliability, backup, and recovery capabilities. Improve self-service workflows and developer experience while collaborating with application teams and stakeholders.

Design, build, and operate scalable logging and operational data platforms across on-premise, government cloud, AWS, Azure, and hybrid environments. Responsibilities include log ingestion, routing, enrichment, storage, indexing, search, streaming, data integration, Infrastructure as Code, CI/CD, security, governance, reliability engineering, monitoring, incident response, and operational support. The role also defines schemas, data contracts, retention policies, platform interfaces, and reusable capabilities for downstream engineering, analytics, dashboards, and automation consumers.

Designs and operates observability across on-premises, cloud, hybrid infrastructure, applications, and networks. The role establishes OpenTelemetry instrumentation standards, telemetry collection, dashboards, SLIs, SLOs, alerting, and operational health indicators. Responsibilities include integrating telemetry sources, supporting incident investigation, improving service reliability, managing retention and security requirements, automating deployments with Infrastructure as Code, and maintaining runbooks. The engineer collaborates with application, infrastructure, network, security, platform, and operations teams and participates in on-call support.

Build and operate production-grade data pipelines across on-premises and cloud environments. Responsibilities include data ingestion, ETL/ELT, transformation, modelling, quality validation, lineage, monitoring, secure integration, and reusable analytical data products. The role supports dashboards, reporting, machine learning, and operational analytics while implementing CI/CD, Infrastructure as Code, data governance, reliability controls, incident response, and production support.

Own and improve shared CI/CD capabilities, reusable deployment pipelines, security and quality gates, and post-deployment verification across on-premise and cloud environments. Build Terraform/OpenTofu and Ansible automation, support engineering teams with self-service delivery tools, and troubleshoot build, test, and deployment failures. Introduce responsible AI-assisted code review, testing, security analysis, documentation, and workflow diagnosis while ensuring delivery processes are secure, reliable, auditable, and recoverable.

Build and operate cloud infrastructure, deployment pipelines, and reliable production systems for Singapore government digital services. Responsibilities may include Infrastructure as Code, CI/CD, containerisation, cloud networking, observability, monitoring, incident response, security controls, compliance, and deployment automation. Engineers may also support application modernisation, system integration, data pipelines, and reusable platform capabilities across greenfield and legacy environments.

Design, develop, test, deploy, monitor, troubleshoot, and resolve issues in software applications and infrastructure supporting government digital services. Participate in code reviews focused on quality, security, and performance, and collaborate with UX designers to deliver user-centered government platforms. The role involves frontend development with React.js, modern JavaScript web applications, backend development, testing, databases, containerization, CI/CD, cloud deployment, and Agile software delivery.

Manage end-to-end software project lifecycles for Singapore government clients, including planning scope, timelines, budgets, resources, stakeholder engagement, governance, quality control, risk resolution, and delivery. Incorporate user feedback, coordinate internal teams and vendors, monitor expenditures, and provide project status updates. The role favors product management experience, an Agile mindset, AWS experience, and strong technical capabilities.

Gather and document business and functional requirements, analyze processes, recommend technology solutions, facilitate stakeholder workshops, and prepare specifications, workflows, and user stories. Collaborate with product owners, architects, developers, operations teams, and vendors throughout delivery. Support Agile planning, solution design, testing, UAT, deployment, impact assessments, governance, security compliance, rollout, and user adoption for enterprise application and digital transformation projects.

Build responsive web applications and scalable frontend/backend services, design secure APIs and databases, implement AI-powered search and asset management features, collaborate on system architecture, and optimize performance and UX for a cloud-native DAM platform.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Design, build, and operate an in-house AIOps/ML/LLM platform across cloud and on-prem Kubernetes. Implement production ML/LLM workflows (training, deployment, inference, monitoring, rollback). Troubleshoot across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers. Collaborate with cross-functional teams to translate client, security, and compliance requirements into platform designs.

Develop responsive, accessible frontend and admin interfaces using React and TypeScript. Build reusable components, integrate REST APIs, implement state management and routing, support AWS deployments, remediate frontend security findings (XSS, token handling), and support unit and UI automation testing.

Design, build, and maintain scalable production pipelines and services to process clinical-grade 30x whole genome data. Optimize performance, reliability, and cost across AWS and hybrid environments. Develop tools and services (Python, C#, SQL, AWS) for genomic analysis, collaborate with cross-functional teams, ensure testing, observability, and validated output, and support consumer-facing DNA apps and reports.

Embed with strategic customers to prototype, build, and productionize full-stack AI and agentic applications; advise on AI strategy, codify repeatable architectures and playbooks, mentor teams, and feed product feedback to improve enterprise AI platforms.

Leads development of commercial genomic data-analysis software and scalable cloud-native pipelines. Converts statistical genetics methods into high-performance products, designs lakehouse architectures and distributed workflows, delivers APIs and scientific user interfaces, and optimizes reliability, performance, and cloud cost. Partners with scientists, infrastructure teams, and product management while setting technical direction, mentoring engineers, and ensuring numerical accuracy, reproducibility, governance, and commercial-grade delivery.

Join a small team to improve a tech product aimed at upskilling users in MENA, working with JavaScript and Node.js in a collaborative remote environment.

Lead delivery of data and digital consulting services: define deliverables, engage senior stakeholders, manage small teams, design scalable data pipelines, implement dashboards and analytics, ensure data quality and governance, and drive digital transformation using cloud, BI, and automation tools.

The Principal Machine Learning Engineer will design and implement AI systems for game development, manage complex workflows, and optimize production models using Python and AWS.

Design, develop, and maintain cybersecurity automation scripts; integrate security solutions, support CI/CD pipeline security, and enhance engineering tools.

The role involves designing and developing cybersecurity automation scripts, integrating security solutions, supporting CI/CD security, and enhancing engineering tools in a dynamic environment.

This role involves designing and maintaining data pipelines, managing APIs, collaborating with teams, and ensuring data quality and governance.