Designs, engineers, secures, and operates enterprise-scale data, analytics, AI, and agentic platforms on GCP. Leads cloud architecture, infrastructure automation, IAM and governance, Kubernetes operations, CI/CD, DevSecOps, production troubleshooting, and reliability improvements. Acts as an escalation point for complex issues, establishes engineering standards, leads design reviews, mentors engineers, and evaluates emerging cloud and AI technologies.
Business Function
Group Technology enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group Technology, we manage the majority of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels.
Responsibilities
- As a Cloud Engineer (Data & AI Platform), you will be responsible for the design, engineering, security, reliability and operational excellence of enterprise-scale data, analytics, AI and agentic platforms on Google Cloud Platform (GCP). You will partner with architects, data engineers, data scientists, security and governance teams to deliver scalable, secure and resilient platform capabilities for analytics, AI/ML, agentic and business-critical workloads. You will provide technical leadership, establish engineering standards and serve as an escalation point for complex production issues.
- Design, implement and operate scalable, resilient and cost-efficient data, analytics, AI and agentic platform capabilities on GCP.
- Build reusable infrastructure and deployment patterns that support the development, productionisation and operation of enterprise AI and agentic workloads, including agent frameworks and model-serving environments.
- Design secure and scalable integration patterns connecting AI agents with enterprise data sources, APIs, tools and business systems.
- Design and implement identity, access and governance controls for human and non-human identities, including service accounts, workload identity, federation and delegated access.
- Define least-privilege, policy-driven access patterns for AI services, enterprise data, APIs and platform resources.
- Lead technical design discussions, architecture reviews and engineering governance; define standards and operational controls for production workloads.
- Implement cloud-native engineering practices including Infrastructure as Code, CI/CD, automated testing and DevSecOps controls.
- Lead troubleshooting and remediation of complex production issues across cloud infrastructure, data platforms, Kubernetes and distributed systems; act as the L3 escalation point, lead root cause analysis and implement permanent corrective actions.
- Mentor engineers, conduct design and code reviews, promote engineering best practices and evaluate emerging cloud and AI technologies to improve platform capabilities.
Requirements
- 8+ years of infrastructure, platform or cloud engineering experience, including at least 5 years working with GCP.
- Strong hands-on experience designing, implementing and operating enterprise-scale solutions on GCP, including production workloads in hybrid cloud environments.
- Experience engineering platforms that enable AI/ML, Generative AI or agentic workloads, including reusable platform services, deployment patterns or model-serving capabilities.
- Expertise in BigQuery, Dataproc, Cloud Storage and cloud-native data platforms, with strong knowledge of Spark and data formats such as Avro and Parquet.
- Hands-on experience with Kubernetes, Docker and containerised workloads in production environments.
- Hands-on experience with Terraform, Infrastructure as Code, CI/CD and cloud automation practices.
- Strong understanding of cloud IAM, service accounts, federated identities, workload identity, delegated authorisation and secure machine-to-machine or application-to-application access models.
- Experience implementing enterprise security controls, including least-privilege IAM, encryption, secrets and key management, policy-based access controls and workload-level security automation.
- Knowledge of data governance and secure data-access patterns, including fine-grained access controls and entitlement-aware authorisation.
Nice to Have
- Google Professional Cloud Architect, Professional Cloud DevOps Engineer or Professional Data Engineer certification.
- Experience leading engineering squads or serving as technical lead for cloud initiatives.
- Development experience in Python and/or Java.
- Experience with Kafka and event-driven architectures.
- Experience with OpenTelemetry, Grafana or Prometheus.
- Experience with SQL platforms including PostgreSQL and MySQL.
- Experience supporting regulated enterprise or financial-services environments.
Location:
DBS Asia HubJob:
TechnologySchedule:
RegularEmployee Status:
Full timeDBS Bank Ltd Singapore, Singapore, SGP Office
Singapore, Singapore
DBS Bank Ltd Singapore Office
Singapore
DBS Bank Ltd Singapore Office
Singapore
DBS Bank Ltd Singapore Office
Singapore
DBS Bank Ltd Singapore Office
Singapore
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