Description
We are seeking a Back End Engineer to join the Group AI Platform team within HSBC's Chief Technology Office (CTO).
The Group AI Platform team builds shared AI foundations that enable teams across the Bank to deliver AI solutions faster, more safely and at scale.
In this role, you will:
- Design, develop, test, deploy and support production-grade services covering agent execution, workflow orchestration, memory, context, MCP integrations, policy enforcement and platform APIs.
- Design and develop backend services for:
- Agent runtimes and execution.
- Workflow orchestration and state management.
- Tool and function calling.
- Prompt, model and workflow management.
- Memory and context management using technologies such as Redis.
- Retrieval-augmented generation.
- Agent triggers and platform APIs.
- Develop and integrate Model Context Protocol (MCP) servers, MCP gateways and agent tools.
- Build event-driven and workflow-based capabilities using Temporal, Google Cloud Pub/Sub and BigQuery.
- Design persistence and data services using PostgreSQL, Redis and BigQuery.
- Evaluate and integrate suitable frameworks and components developed by other HSBC teams.
- Use frameworks such as LangChain, LangGraph, LlamaIndex or equivalent technologies where they provide clear value, while avoiding unnecessary framework or vendor lock-in.
- Contribute to backend reference architectures, reusable patterns and engineering standards.
To be successful in this role you should have the following skills:
- Significant experience in backend, software or platform engineering using Python and Go, as well as Kubernetes, Docker & CI/CD.
- Experience designing and operating distributed, microservice-based systems.
- Strong understanding of APIs, event-driven architecture, workflow orchestration and cloud-native engineering.
- Experience with one or more of:
- Generative AI and LLMs.
- Agentic systems.
- MCP.
- RAG.
- Tool or function calling.
- Prompt and workflow management.
- Experience with Redis, PostgreSQL, Temporal, Pub/Sub or BigQuery is advantageous.
- Experience deploying and supporting AI products in production is preferred; production experience with non-AI platforms is essential.