JOB PURPOSE

Data & AI Architect reports to China Enterprise Architect Director. This role is primarily focused on enterprise data‑platform architecture for China, covering platform capability design, platform standardisation, governed data products, data integration patterns, together with agentic‑AI enablement on top of the data platform. The role drives alignment between China‑local data platform and global reference data‑AI platforms, and delivers justified local adaptations to satisfy China‑specific data regulatory and business requirements. The senior architect contributes to enterprise architecture governance, leads architecture reviews for complex data‑platform and AI‑enabled initiatives.

 DUTIES & RESPONSIBILITES

1. Enterprise Data‑Platform Architecture & Data Products

• Define governed enterprise data‑platform architecture, platform capability blueprint, reusable data products, data integration patterns, and agentic‑AI enablement patterns built upon China data platforms.

Produce data‑platform architecture diagrams, platform component specifications, data‑product definitions and architecture decision records for both local and global stakeholders.

2. Global Alignment & China Localisation

• Drive alignment of China local data platform with global enterprise data‑AI reference platforms; design and document justified local adaptations for China‑specific data regulation, residency requirements and business needs.
• Establish local data‑platform architecture standards, platform component selection guidelines, data governance principles and operational guardrails, balancing global enterprise framework and China regulatory constraints.

3. Enterprise AI Solution Architecture

• Define end-to-end architectures for enterprise AI solutions, including model access, retrieval-augmented generation (RAG), agent orchestration, tool integration and connections to enterprise applications and governed data services.

• Establish architecture patterns and guardrails for AI evaluation, deployment, versioning and monitoring, addressing output quality, security, privacy, access control and human oversight in collaboration with relevant governance teams.

• Define architecture patterns for model training, fine-tuning and inference, including training data preparation, experiment tracking, model evaluation, deployment and lifecycle management, in collaboration with data science and machine learning engineering teams.
• Guide and validate AI solution optimisation across model selection, prompt design, retrieval quality, agent workflows and model fine-tuning where appropriate, balancing business outcomes, accuracy, reliability, latency and cost.

4. Architecture Governance & Review

• Participate in enterprise architecture governance; lead architecture review for all major China data‑platform, data product and AI‑enabled projects.
• Evaluate data‑platform technical risks, platform capacity limits, technical debt, compliance gaps and performance bottlenecks; propose platform modernisation and improvement roadmaps.

5. Innovation & Continuous Improvement

• Evaluate emerging data and AI technologies; design and conduct hands-on proofs of concept (PoCs) to validate feasibility, business value, architectural fit and operational readiness, and recommend adoption based on defined success criteria.
• Continuously assess and evolve existing data and AI platform architectures; drive agreed improvements in performance, resilience, scalability, observability, maintainability and cost efficiency with engineering and operations teams.

6. Stakeholder Collaboration & Delivery Guidance

• Collaborate closely with global data‑platform teams, local data engineering, platform operation, business and delivery teams to guide end‑to‑end data‑platform design, implementation and platform lifecycle management.

JOB REQUIREMENTS & COMPETENCIES

- Bachelor's degree or above in Computer Science, Data Science or related technical discipline.

- Deep practical knowledge of modern enterprise data platform architecture, including platform component design, data modelling, data integration, data governance and data product lifecycle.

- Hands on experience working with mainstream data platform technologies: data lakes, lake house, warehouses, data pipelines, catalog, access control and API oriented data services.

- Practical understanding of agentic AI and generative AI enablement scenarios running on top of enterprise data platforms.

- Ability to assess global reference data platform capabilities, identify gaps and design necessary local adaptations for China specific compliance and operational constraints.

- Capable of leading architecture reviews for complex data platform initiatives and delivering comprehensive architecture documentation..

Essential：    

- 5+ years data architecture / AI architecture experience; enterprise data product & governance experience required.

- 5+ years enterprise grade data platform and data architecture experience; proven track record in enterprise data platform project delivery, data governance and reusable data product delivery.

Desirable：

- Multinational enterprise data platform architecture working experience.

- Solid experience in end to end project delivery.

- Hands on project experience within life science / pharma industry data domain.

- Relevant data architecture or platform certifications are a plus.

Others

- Strong awareness of China data security, personal information protection and data residency regulatory requirements.

- Excellent cross region collaboration to align requirements with global data platform teams..

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.