AI Data Engineer

Principal responsibilities：

• Build and operate pipelines for structured domain data (onboarding events, customer/entity data, credit facilities/exposures, limits, risk grades, portfolio hierarchies, performance and arrears).

• Build unstructured data pipelines for domain documents (KYC documents, corporate registries extracts, credit memos, financial statements, portfolio review materials): parsing, metadata enrichment, deduplication and retention handling.

• Develop embedding/vectorisation pipelines and manage vector indices with refresh and deletion strategies aligned to data governance.

• Implement data quality, observability and lineage: automated testing, SLAs, anomaly detection, monitoring and runbooks.

• Apply privacy-by-design: PII handling, masking/tokenisation where required, access controls and audit trails.

• mplement dataset versioning and reproducibility (snapshots, schema evolution, data contracts) to support repeatable evaluations.

• Partner with Agent Engineers and stakeholders to define data requirements and evaluation datasets; close feedback loops from production retrieval/agent performance.

• Support production operations and continuous improvement of data services powering AI.

Knowledge & Experience / Qualifications：

• Delivering reliable, fresh, well-governed datasets and indices used by KYC, credit and portfolio AI solutions.

• Improving retrieval relevance and answer grounding by strengthening metadata, chunking inputs, and data quality.

• Reducing operational incidents through strong testing, monitoring, and disciplined change management.

• Making AI datasets reproducible and auditable to support reviews and control expectations.

What additional skills will be good to have? ：

• Experience working with sensitive data domains (KYC/AML, credit risk) and implementing controlled access patterns.

• Familiarity with vector search concepts, document ranking/reranking and retrieval metrics.

• Experience with feature stores or MLOps-style dataset management.