HSBC
Associate Director, Software Engineering (Knowledge & RAG Platform Engineering Lead)
- Compensation
- Not disclosed
- Location
- Guangzhou, Guangdong, China
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Devops
- Posted
Skills
Must have
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Management, Data Engineering, AI, or a related technical discipline; 7+ years of hands-on engineering experience in
- Proven experience as a senior technical IC or platform technical lead, owning architecture, engineering standards, design review, implementation quality, optimisation, and production readiness for com
- Deep knowledge of knowledge intelligence and management practices, including taxonomy/ontology design, metadata standards, knowledge refinement, lifecycle management, governance, lineage, and enterpri
- Hands-on experience with knowledge graph, semantic search, RAG, agentic AI, or data enrichment systems, plus strong engineering foundations in distributed systems, APIs, data pipelines, observability,
- Strong security, compliance, production, and stakeholder mindset, with the ability to adopt industry best practices, mentor engineers, influence cross-functional teams, and apply AI-native approaches
Full description
Some careers have more impact than others.
If you’re looking for a career where you can make a real impression, join HSBC and discover how valued you’ll be.
We are currently seeking an experienced professional to join our team in the role of Associate Director, Software Engineering (Knowledge & RAG Platform Engineering Lead).
Business: CTO Platforms (AI Platforms)Job ID: 56078Location: Guangzhou / Xi'anPrincipal responsibilities
- Senior technical IC leadership for knowledge intelligence: Act as a hands-on senior technical expert in knowledge intelligence and knowledge management, shaping architecture, technical direction, and implementation approach for an enterprise-grade knowledge platform.
- Knowledge platform architecture & delivery ownership: Lead the design and build of scalable platform capabilities across ingestion, extraction, enrichment, classification, knowledge asset management, discovery, reuse, governance, and controlled downstream consumption.
- Industry best practice adoption & engineering standards: Evaluate knowledge management, knowledge engineering, ontology/taxonomy, graph, and AI-assisted curation practices, and translate them into engineering standards, reusable patterns, reference architectures, APIs/SDKs, and runbooks.
- Knowledge refinement & intelligence workflows: Lead workflows that extract, refine, summarize, classify, deduplicate, consolidate, and contextualize fragmented information into trusted, structured, traceable, and business-aligned knowledge assets.
- Knowledge graph & semantic layer leadership: Architect and evolve ontology/taxonomy management, entity/relation extraction, entity resolution, graph modeling, graph traversal, lineage-aware context assembly, and graph-based reasoning support for AI and search scenarios.
- Knowledge lifecycle, governance & self-checking: Establish mechanisms for ownership, validation, versioning, publishing, freshness monitoring, deprecation, retention, rollback, quality scoring, duplication/contradiction detection, and citation/provenance validation.
- Optimisation & operational readiness: Define SLOs/SLAs, observability, alerting, capacity planning, cost controls, incident response, troubleshooting playbooks, resilience patterns, and continuous production improvement for knowledge platform services.
- Cross-team influence & enablement: Partner with AI engineering, data platforms, cybersecurity, architecture, product, and domain SMEs to drive adoption, mentor engineers, review designs, unblock complex issues, and communicate trade-offs to senior stakeholders.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Management, Data Engineering, AI, or a related technical discipline; 7+ years of hands-on engineering experience in backend, data, AI, knowledge, search, or enterprise information platforms.