# Staff Engineer – AI Data Engineering at Prudential plc

Prudential plc is hiring a Staff Engineer – AI Data Engineering. Level rates it Works on AI ●●●○; you can [apply on Prudential plc](https://jobsbylevel.com/go/0080c404-49e6-49e9-8ef7-70144fc99418?utm_medium=ai-agent).

AI Level 3, AI centrality 75 out of 100. Singapore (Group Head Office).

## Details

- Company: [Prudential plc](https://jobsbylevel.com/companies/prudential-plc)
- AI level: AI Level 3 (score 75 out of 100)
- Location: Singapore (Group Head Office)
- Posted: November 2, 2026
- Apply: https://jobsbylevel.com/go/0080c404-49e6-49e9-8ef7-70144fc99418?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=staff-engineer-ai-data-engineering-at-prudential-plc-f3e6a1

## Prepare for this job

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## Description

Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed. We are looking for an experienced AI Data Engineering Lead to define and lead the engineering of enterprise-scale data platforms and data products that power Artificial Intelligence, Machine Learning, Generative AI and advanced analytics. This individual will build the organisation’s AI-ready data foundation by bringing together modern data engineering, cloud platforms, batch and real-time processing, unstructured data, vector and semantic technologies, data governance, and AI/ML engineering practices. The role requires a senior technology leader with 15+ years of experience, strong hands-on technical depth, architecture capability, and a proven ability to lead engineering teams and deliver complex data platforms at enterprise scale. Key Responsibilities AI Data Engineering Strategy & Architecture · Define the enterprise AI Data Engineering strategy, architecture and engineering standards. · Design scalable data architectures supporting AI/ML, GenAI, LLM, analytics and enterprise data products. · Establish reusable architectural patterns for structured, semi-structured and unstructured data. · Define patterns for data lakes, lakehouse architectures, data warehouses, streaming platforms, vector databases and AI knowledge layers. · Drive architectural decisions covering performance, scalability, resiliency, security and cost. AI-Ready Data Platforms · Lead engineering of data platforms providing trusted and reusable data for ML models, LLMs, AI agents and GenAI applications. · Build ingestion and transformation frameworks for data from databases, APIs, documents, events, logs and enterprise applications. · Establish pipelines for batch, near-real-time and streaming data processing. · Drive automated data validation, quality controls, metadata management and lineage. · Establish engineering approaches for preparing text, documents, images and other unstructured data for AI consumption. GenAI & LLM Data Engineering · Provide technical leadership for Retrieval-Augmented Generation (RAG), document ingestion, parsing, chunking and enrichment. · Lead embedding generation and management, vector and hybrid search, semantic retrieval and reranking. · Design knowledge repositories, metadata and access-control filtering, and enterprise search integration. · Create data pipelines for LLM evaluation, AI grounding, authorised retrieval, fine-tuning where applicable, and AI agents. · Understand how poor retrieval, incomplete or stale data, weak metadata and inappropriate chunking affect enterprise AI reliability. Modern Data Engineering · Provide leadership across ETL/ELT, distributed processing, lakehouse and data warehouse architectures, streaming, event-driven design, data APIs and data services. · Drive data modelling, data contracts, observability, quality engineering, lineage and metadata management. · Embed Infrastructure-as-Code, CI/CD, DevOps and DataOps practices in the engineering lifecycle. · Improve reusability, automation, reliability and developer productivity through common frameworks and standards. Cloud & Technology Platforms · Demonstrate significant expertise across modern cloud and data platforms such as Microsoft Azure, Azure Databricks, Microsoft Fabric, Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, Snowflake and Databricks. · Bring strong capability in Python, SQL, Apache Spark/PySpark, Kafka, Delta Lake, Airflow and dbt. · Apply AI and GenAI data technologies including Azure AI Search, vector databases, embedding models, semantic search,

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/staff-engineer-ai-data-engineering-at-prudential-plc-f3e6a1

Source: https://jobsbylevel.com/jobs/staff-engineer-ai-data-engineering-at-prudential-plc-f3e6a1

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Level. https://jobsbylevel.com/jobs/staff-engineer-ai-data-engineering-at-prudential-plc-f3e6a1.

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