14 hours ago
Guangzhou, ChinaStaff+
Responsibilities
- Identify high-impact opportunities to apply AI across mobile engineering design, coding, testing, release readiness, and incident response.
- Build and operationalize AI-assisted workflows, reusable accelerators, prompt patterns, evaluators, automation scripts, and agentic workflows.
- Design, build, and maintain reliable data pipelines, curated datasets, data models, quality checks, lineage, monitoring, and documentation.
- Create dashboards and decision tools for mobile engineering health, including stability, performance, delivery, quality, customer impact, regressions, and anomaly detection.
- Define KPIs, targets, measurement frameworks, and leading indicators that drive measurable engineering and customer-experience improvements.
- Lead AI enablement through training sessions, playbooks, demonstrations, mentoring, documentation, FAQs, and troubleshooting guides.
- Advise engineering teams on AI tool selection, workflow design, output evaluation, governance, security, and common AI risks.
- Support the Mobile CTO with AI-native engineering strategy, investment prioritization, governance, and value tracking.
- Collaborate with architecture, security, compliance, product, engineering, risk, and technology strategy stakeholders.
Requirements
- Proven ability to apply AI to practical engineering problems involving design, coding, testing, analysis, and automation.
- Strong problem-solving, communication, stakeholder influence, and cross-functional collaboration skills.
- Strong experience building production-grade data pipelines and datasets with reliability, observability, and data quality controls.
- Proven ability to build dashboards and analytics products that drive decisions and action.
- Strong understanding of secure data handling and engineering practices in regulated environments.
- Proficiency in Python and SQL for data engineering and applied AI workflows.
- Hands-on experience with LLMs, prompt engineering, retrieval-augmented generation concepts, evaluation techniques, and agentic or automation workflows.
- Experience with analytics and visualization tools such as Power BI, Tableau, Looker, Grafana, or equivalent.
- Familiarity with CI/CD and developer toolchains, including integrating data and AI into Git-based pipelines, test systems, and build tooling.
- Understanding of experimentation, A/B testing concepts, KPI definition, causal thinking, and guardrails.
- Experience working in Agile delivery environments and with product, engineering, risk, and security stakeholders.
Benefits
- Flexible working and opportunities for continuous professional development and career growth.
- Inclusive and diverse workplace with equal-opportunity employment practices.
Categories
AI ApplicationsData Engineering
About HSBC
HSBC is a global bank that provides retail, commercial and investment banking, payments, and wealth management to individuals, SMEs, and multinationals. It earns fees, interest income, and trading revenues through branches and digital platforms across major markets. Headquartered in London, it serves over 40 million customers in 58 countries and is listed in London, Hong Kong, New York, and Bermuda.