AI & Data Architect

The AI & Data Architect sits within Carlyle’s Enterprise Technology & Data organization and supports firm-wide data and AI initiatives spanning investment platforms, portfolio operations, investor relations, and corporate functions. The role operates within a federated data operating model, partnering with domain teams while establishing shared architectural standards and platforms for data and AI. The AI & Data Architect is a senior technical leader and trusted advisor to the Head of Data Transformation, serving as Carlyle’s primary architectural authority at the intersection of enterprise data and AI. This role is responsible for designing, evolving, and governing Carlyle’s data and AI architecture end to end. The AI & Data Architect will ensure that enterprise data is trusted, well-governed, context-rich, and readily usable by analysts, applications, LLMs, agents, generative AI products, and analytical copilots. The role requires direct, hands-on experience building generative AI systems and applying an AI-forward lens to architectural decisions. This includes defining how enterprise data is retrieved by agents, how semantic layers support natural-language analytics, and how lineage, governance, and controls extend to model inputs and outputs. This is a senior individual-contributor architecture role that translates Carlyle’s data and AI strategy into executable technical designs, bridging strategy, engineering execution, governance, and business value across a federated operating model. What Success Looks Like In the first 12 months, this role will help define Carlyle’s target-state data and AI architecture, establish reusable patterns for retrieval and semantic access, strengthen governance for AI-consumable data, and guide priority AI and data initiatives from architecture through execution. In-Office Requirement: 4 days per week AI-Ready Data Foundations & Semantic Layer (≈35%) Architect AI-ready data foundations - semantic layers, contextual metadata, data contracts, and retrieval-ready knowledge stores - that allow LLMs, agents, and generative AI applications to reason reliably over Carlyle’s data. Design and govern enterprise patterns for retrieval-augmented generation (RAG), vector stores, embedding pipelines, chunking strategies, and grounding approaches for AI use cases across the firm. Define how agents and copilots discover, query, and act on enterprise data, including tool and function interfaces, query routing, and architectural guardrails. Partner with Data Science and AI Engineering teams on feature stores, evaluation environments, and reusable AI data products. Advance semantic modeling and context engineering to enable natural-language analytics, conversational reporting, and AI-driven insights for the business. Enterprise Data Architecture & Modernization (≈30%) Act as the senior technical authority for enterprise data and AI architecture, partnering closely with the Head of Data Transformation to shape and execute Carlyle’s combined data and AI strategy. Design and evolve Carlyle’s cloud-native, AI-ready data platform, supporting analytics, reporting, automation, and generative AI at enterprise scale. Define target-state architectures for data ingestion, transformation, storage, semantic layers, retrieval, and consumption across federated domains, with AI readiness and governed consumption as first-class design requirements. Establish and enforce architectural standards for scalability, performance, security, resiliency, and cost efficiency across both data and AI workloads. Modern Data & AI Pipelines and Platforms (≈20%) Architect and guide the implementation of modern data and AI pipelines using tools such as dbt, Fivetran, Apache Iceberg, Snowflake, and Databricks, alongside MLOps/LLMOps platforms, AI gateways, feature stores, and vector databases (e.g., MLflow, Databricks Vector Search, pgvector, Pinecone). Design ELT, streaming, and embedding/indexing pipelines that are