Assoc, AI Core DevOps Engineer, Data Platform, Group Technology

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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