Top AWS Jobs in Singapore

Plans and executes full-scope red team and adversary emulation operations, including phishing, C2, privilege escalation, lateral movement, EDR/XDR evasion, Active Directory and cloud compromise, and social-engineering scenarios. Develops custom offensive tooling, collaborates with Blue Teams to validate detections, documents attack chains, and provides remediation guidance. Requires 5+ years of offensive security experience, advanced scripting skills, and recognized offensive security certifications, with bug bounty or CTF achievements preferred.

Build and maintain production-grade AI applications for Singapore’s land transport network. Integrate LLMs, computer vision, RAG pipelines, and agentic systems; optimize models and inference engines; develop APIs, microservices, and data pipelines; implement guardrails, evaluation, observability, and safety testing; and support cloud-native deployment and continuous improvement in collaboration with technical teams and industry partners.

Leads APAC public cloud sales strategy and revenue growth, manages Cloud Business Development Managers, builds hyperscaler and channel partnerships, supports major opportunity closure, develops cloud solutions, trains sales teams, forecasts pipeline, and delivers quarterly sales targets.

Build and operate production custody systems: signing service, key management, chain integrations, and transaction pipelines. Own end-to-end subsystems, integrate new chains, collaborate with cryptography on threshold signing and key recovery, write post-mortems, and produce clear async design docs and PRs.

Build and operate reliable production ML and AI systems across AWS and Databricks. Responsibilities include developing MLOps components, productionizing models, creating data and feature pipelines, optimizing Spark and SQL workloads, automating CI/CD and retraining, implementing governance and security controls, and establishing monitoring, evaluation, rollback, and incident-response practices. The role also supports generative AI, recommendation, and search workloads while improving reusable engineering patterns and transferring knowledge to internal teams.

Design, build, and operate production-grade batch and streaming data pipelines across AWS and Databricks. Develop lakehouse architectures, data models, quality controls, orchestration, lineage, monitoring, and security practices. Optimize Spark and SQL workloads, establish engineering standards, review designs and code, mentor engineers, and collaborate with data owners, analysts, data scientists, architects, and governance stakeholders. Support reliable, well-governed data products for analytics, reporting, and machine learning.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Lead and scale the engineering organization for a regulated digital-asset custody platform. Own hiring, org design, engineering operations (on-call, incidents, release hygiene), delivery against roadmaps, audit and regulator readiness (SOC 2, SAMA, ISO 27001), and cross-functional alignment. Amplify an existing small, specialized team to meet regulatory and institutional requirements while preserving culture and retention.

Own the organization’s multi-cloud platform across Google Cloud, Alibaba Cloud, AWS, Cloudflare, and Vercel. Manage Kubernetes clusters, infrastructure as code, CI/CD, blockchain node fleets, networking, observability, security, incident response, developer enablement, and cloud cost optimization. Operate production infrastructure at scale, improve reliability and security, automate deployments, and support validator, RPC, archive, GPU, confidential-computing, and database workloads.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Build and scale Supabase Storage across client libraries, CLI, CDN, storage servers, and deployment infrastructure. Responsibilities include improving upload and download performance, asset optimization, edge networking, caching, observability, reliability, APIs, developer tools, and the storage explorer. The role also involves PostgreSQL and distributed-systems performance tuning, infrastructure operations, escalated support resolution, and end-to-end project ownership in an asynchronous, remote, open-source environment.

Build and support low-latency C++ trading infrastructure on Linux, including pricing engines, connectivity gateways, market-data systems, messaging layers, and FIX integrations. Optimize microsecond-level performance, develop high-throughput systems, contribute to AWS cloud-native infrastructure, and maintain observability. Own the full software lifecycle from design through production support, incident response, testing, deployment, and documentation.

Designs secure, scalable, and highly available cloud and payment solutions for Singapore’s Automatic Fare Collection system. Responsibilities include translating requirements into technical specifications, conducting impact analyses, leading cloud migration strategies, developing architecture and integration designs, governing security and resiliency practices, and coordinating with internal teams, vendors, and financial institutions. The role also involves architecture reviews, technical presentations, and assurance across complex multi-system environments.

Design, implement, commission, secure, monitor, and maintain resilient network infrastructure supporting Singapore’s rail and bus automatic fare collection systems. The role covers LAN, WAN, WLAN, cloud, datacentre, security controls, backups, disaster recovery, troubleshooting, upgrades, documentation, automation, and incident recovery. The engineer collaborates with project teams, cybersecurity specialists, vendors, and operators, and may support onsite cutovers and after-hours maintenance.

Develop, modernize, test, deploy, and support secure AWS-based back-office applications for Singapore’s automated fare collection system. Responsibilities include requirements analysis, software design, Java and Spring development, documentation, troubleshooting, performance improvement, production support, and collaboration with developers, users, and testers. The role requires experience with full software development lifecycles, AWS services, containerized applications, databases, messaging platforms, CI/CD, and regulated cloud environments.

Own end-to-end testing for an integrated e-payment system, including requirements analysis, test planning, case design, environment preparation, execution, defect tracking, automation development, stakeholder reviews, and root-cause analysis. Build CI/CD test automation solutions, improve test efficiency, and address production, user acceptance, and coverage gaps.

Lead the Camunda workflow practice and build end-to-end digital solutions from discovery through production. Model BPMN and DMN processes, develop Zeebe workers and connectors, integrate enterprise systems and event streams, establish governance and reusable patterns, and create auditable analytics. The role combines hands-on backend engineering, business analysis, architecture, stakeholder engagement, mentoring, and technical leadership across a logistics product suite.

Serve as the customer-facing security lead for delivered applications and AI-enabled solutions. Coordinate threat modeling, risk assessments, security testing, audits, remediation, documentation, and incident response. Advise teams on secure cloud and AI solution design, evaluate third-party security work, maintain risk registers, ensure compliance with security standards, and manage customer and stakeholder communications. The role requires technical security expertise, governance experience, certification, and availability for after-hours incident response.

The DevSecOps Engineer at Kyndryl designs and automates infrastructure and deployment pipelines, ensuring security and scalability in cloud environments. Responsibilities include troubleshooting, configuring CI/CD pipelines, and implementing security best practices.

Develop tools and infrastructure for training language models in interactive environments, implement novel methods in language modeling, reinforcement learning and multi-agent systems, and contribute to publications and open-source projects.

Leads NTU Singapore’s technology audit capability, including risk-based audit planning, cybersecurity and IT control assessments, cloud and enterprise systems audits, data governance, digital transformation, and AI initiative reviews. The role presents technology risks to senior leadership and the Audit & Risk Committee, develops audit methodologies and analytics, coaches auditors, manages external specialists, and evaluates controls across research and university technology environments.

Lead software engineering work for JPMorganChase’s Post Trade Technology team, designing, developing, testing, troubleshooting, and supporting secure, scalable production applications. The role involves code review, operational stability improvements, cloud-native development, SDLC practices, agile delivery, and evaluating vendor architectures. It also leads responsible adoption of AI-assisted engineering tools for coding, testing, code review, incident analysis, and automation.

Build and maintain customer lifetime value, churn, conversion, clustering, classification, and sentiment analysis models across APAC markets. Partner with commercial, marketing, and finance teams to translate business questions into analytical solutions, present insights to non-technical stakeholders, and manage the full data science lifecycle from problem framing and data extraction through deployment and monitoring.

Build and evaluate benchmark datasets for LLM systems processing patent documents and deal-sourcing data. Responsibilities include reviewing and refining annotations, running blind-test evaluations, measuring extraction reliability, identifying hallucinations and edge cases, cataloging schemas, and preparing quality-acceptance reports. The role offers direct engineering supervision, stakeholder-facing projects, and deployment experience.