全职 Assoc, AI Core DevOps Engineer, Data Platform, Group Technology 工作, 薪水, DBS Bank East Region (Singapore) 公司招聘中 - Ricebowl

Job Responsibility

Working with subject-matter experts (SMEs) across finance, infrastructure, security, risk, and application teams, you will learn their processes and constraints to design AI and agent-based solutions that give the organisation clear, actionable insight. We want a resource who takes full ownership of their work and is keen to learn how enterprise architecture comes together.

Enterprise 360 AI Initiative

• Work with SMEs across domains to map their current processes, data sources, pain points, and limitations.
• Turn business needs into AI and agentic use cases, then propose, prototype, and refine solutions with the team.
• Develop AI models for cost forecasting, anomaly detection, utilisation right-sizing, and compliance risk scoring.
• Design and build AI agents that can answer questions, summarise posture, flag issues, and recommend or trigger actions (for example, "Which applications are over-provisioned and missing DR coverage?").
• Put guardrails, evaluation, human-in-the-loop approvals, and audit trails in place so agents are safe and trustworthy at enterprise scale.
• Turn successful prototypes into reusable, production-grade services and reference patterns.

Full-Stack Application Development

• Build and maintain interactive React-based dashboards and tools that surface AI-driven insights to business users, including data visualisation with charting libraries.
• Develop and extend Python/Flask RESTful APIs that integrate with databases, AI services, and external data sources, with attention to error handling, caching, and security.
• Work across the full stack—from responsive, user-facing UIs to backend SQL query generation, API design, and server-side business logic.

Architecture and Stakeholder Engagement

• Contribute to group-wide initiatives as part of the Architecture team, from early assessment to delivery.
• Help write solution designs, reference architectures, technical standards, and decision records.
• Present findings, recommendations, and trade-offs clearly to both technical and business audiences.

AI Platform Engineering (GCP and On-Prem)

• Build CI/CD and MLOps pipelines for models, agents, and microservices, deploying to GKE and on-prem clusters.
• Deploy and scale model and LLM serving (for example vLLM, Triton, or KServe) and Vertex AI endpoints.
• Integrate with GitOps (ArgoCD or Flux) and observability platforms (Prometheus, Grafana, Cloud Monitoring, OpenTelemetry).
• Apply security and compliance standards across all deployments.

Ownership & Execution

• Own your deliverables end to end: plan, build, document, hand over, and support them.
• Take initiative, raise risks early, suggest improvements, and follow through without being asked.
Keep up with developments in AI, agents, and infrastructure, and bring useful ideas back to the team.

Job Requirements

• 3-6 years of experience in DevOps, platform, MLOps, or AI engineering.
• Hands-on experience building ML models or LLM-based applications in Python.
• Exposure to RAG pipelines, vector databases, LLM evaluation, and guardrails.Exposure to agentic frameworks and patterns such as LangGraph, Google ADK, Vertex AI Agent Builder, CrewAI, tool/function calling, or MCP.
• Experience building interactive web applications with React (hooks, context, component composition) and modern JavaScript/ES6+, including data visualisation with charting libraries such as Recharts or Chart.js.
• Hands-on backend development with Python/Flask (or FastAPI/Django), building RESTful APIs that integrate with databases (MySQL, Trino/Presto) and external services, with attention to error handling, caching, and security.Comfortable working across the full stack—from designing responsive, user-facing dashboards and forms (Bootstrap, styled-components) to implementing API endpoints, SQL query generation, and server-side business logic.
• Hands-on GCP experience (GKE, Vertex AI, BigQuery, IAM) and experience with self-managed Kubernetes.
• Solid SQL and data-wrangling skills for pulling together data from many enterprise sources.
• The confidence to engage SMEs, ask good questions, and turn their processes into technical solutions.
A strong sense of ownership, accountability, and follow-through.Clear written and verbal communication, alongside a genuine curiosity to learn enterprise architecture.

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