Director, AI, Data & Enterprise Architecture

What matters most

• Enterprise AI and Data Governance Frameworks

  The role requires establishing guardrails for the entire AI/ML lifecycle, including MLOps, LLMOps, and model governance. You must prioritize creating standardized patterns for model safety, bias testing, and auditability to ensure that enterprise-wide AI deployments remain compliant and reliable.

• Scalable Modern Data Architecture Design

  You are expected to architect scalable data solutions such as Lakehouse or data mesh models. This involves defining metadata management, lineage, and quality controls that support both real-time streaming and batch processing across the enterprise.

• Strategic Alignment of Technology and Business

  A core priority is translating complex business strategies into actionable multiyear technology roadmaps. You must act as a bridge between technical engineering teams and senior stakeholders to ensure that platform investments deliver measurable business outcomes.

Likely challenges to think through

• Balancing Innovation with Enterprise Rigor

  You will face the challenge of implementing cutting-edge GenAI and RAG solutions while adhering to strict enterprise security and privacy standards. Prepare to discuss how you manage the trade-off between rapid experimentation and the need for production-grade stability and safety filters.

• Driving Cross-Functional Consensus

  As a senior leader, you must align diverse teams including product, engineering, security, and risk. The difficulty lies in enforcing architecture standards without stifling team agility; prepare to demonstrate your approach to facilitating design reviews and managing technical debt in a large-scale environment.

Topics to prepare

• Enterprise AI Governance and Risk Management

  The role requires establishing frameworks for model governance, bias testing, and safety filters. Review the NIST AI Risk Management Framework or similar industry standards to prepare for designing guardrails for LLM and ML deployments.

• Modern Data Architecture Patterns

  The position emphasizes Lakehouse, data mesh, and domain-oriented data products. Study the architectural trade-offs between centralized data warehouses and decentralized data mesh to articulate how you would structure data ownership and accessibility in a large enterprise.

• MLOps and LLMOps Lifecycle Management

  Focus on the end-to-end lifecycle including reproducibility, drift monitoring, and automated evaluation. Prepare to discuss how to integrate CI/CD pipelines with model registries and feature stores to ensure production-grade AI reliability.

Skills to revise

• RAG and Vector Search Implementation

  Practice designing architectures that incorporate vector databases and retrieval-augmented generation. Focus on how to manage context window constraints, hallucination reduction, and prompt versioning in enterprise-scale applications.

• Cloud-Native Infrastructure Design

  The role requires expertise in Kubernetes, containers, and serverless architectures. Review how to deploy scalable AI workloads on cloud platforms (AWS, Azure, or GCP) while maintaining cost-efficiency and security compliance.

• Technical Stakeholder Communication

  Prepare to translate complex architectural decisions into business-aligned roadmaps. Practice explaining the ROI of specific data or AI platform investments to non-technical executive leadership.

• Develop an Enterprise AI/Data Strategy Presentation

  Prepare a high-level presentation demonstrating how you would align AI/ML and data architecture with business strategy. Include a multi-year roadmap that balances immediate production-grade AI needs with long-term data governance, security, and scalability requirements.

• Document a Reference Architecture for RAG and LLMOps

  Create a technical diagram and supporting documentation for a scalable RAG (Retrieval-Augmented Generation) pipeline. Detail your approach to vector search, model evaluation, safety guardrails, and the LLMOps lifecycle within a cloud-native environment.

• Prepare Case Studies on Data Mesh and Modern Platforms

  Draft two detailed case studies from your experience: one focusing on implementing a data mesh or lakehouse architecture, and another on establishing MLOps/CI/CD frameworks. Emphasize how you managed cross-functional stakeholders and ensured data quality and lineage.

• Formulate Strategic Questions for Leadership

  Prepare questions regarding Singtel's current data maturity, the primary business drivers for their AI initiatives, and the existing governance challenges. Focus on how the architecture team currently balances innovation speed with enterprise security and compliance standards.

• Refine Your Approach to Model Governance and Risk

  Outline your framework for model risk management, including how you handle bias testing, explainability, and auditability in production. Be ready to discuss how you translate these technical requirements into policies that non-technical stakeholders can support.