Data & AI Engineer

Fund or Department Description The Data & AI Engineer 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 engineering teams to implement shared platforms and reusable patterns for data and AI under the technical direction of the Senior AI & Data Architect. Position Summary The Data & AI Engineer is an experienced, hands-on engineer who turns Carlyle’s data and AI architecture into working production systems. Reporting to the Senior AI & Data Architect, this role is responsible for building and operating the pipelines, semantic layers, retrieval systems, and AI-ready data products that power analytics, automation, LLMs, agents, and generative AI applications across the firm. The role requires deep, hands-on expertise across modern data engineering and applied AI engineering. The Data & AI Engineer will implement retrieval-augmented generation (RAG) patterns, embedding and indexing pipelines, vector stores, and semantic models alongside core ELT, streaming, and analytical pipelines — treating LLMs, agents, and copilots as first-class consumers of the data platform. This is a senior individual-contributor engineering role that executes again [... source excerpt omitted ...] e. What Success Looks Like: In the first 12 months, this role will deliver foundational AI-ready data pipelines and retrieval components defined in the target-state architecture, productionize one or more priority RAG or agent-grounding use cases, and establish reusable engineering patterns that other domain teams can adopt across the federated data platform. In-office requirement: 4 days per week Location: Washington, D.C. or New York, Responsibilities NYAI Data Pipelines & Retrieval Systems (≈35%) Build and operate AI-ready data pipelines — embedding generation, chunking, indexing, and refresh workflows — that make Carlyle’s enterprise data reliably retrievable by LLMs, age [... source excerpt omitted ...] that powers natural-language analytics, conversational reporting, and AI-driven insights for business users. Modern Data Pipeline Engineering (≈30%) Design, build, and maintain production-grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake. Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency. Write clean, well-tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing. Productionize new sources and domains under the federated operating