About this role
Carlyle’s Enterprise Technology & Data organization seeks a senior individual-contributor Data & AI Engineer to turn enterprise data and AI architecture into production systems. Reporting to the Senior AI & Data Architect, the engineer will build AI-ready pipelines, retrieval systems, semantic layers and reusable data products for analytics, automation, LLMs and agents across investment platforms, portfolio operations, investor relations and corporate functions. The role is based in Washington, D.C. or New York, NY, with four days per week in the office.
Responsibilities
- Build embedding, chunking, indexing and refresh workflows; implement RAG components including vector-store integration, hybrid search, re-ranking and grounding. Develop guarded, logged and evaluable tool interfaces through which agents and copilots can use enterprise data.
- Design and operate production ELT, streaming and transformation pipelines using tools such as dbt, Fivetran and Snowflake. Write tested Python and SQL, and apply version control, code review and CI/CD practices.
- Implement semantic models, data contracts, dimensional models and documented data products for self-service analytics and AI grounding. Work with domain engineers in a federated data operating model.
- Add data-quality checks, lineage, observability, access controls and AI monitoring, including prompt and response capture, retrieval metrics and model-performance signals. Contribute to design reviews, mentor junior colleagues and document reusable patterns. Within the first 12 months, deliver foundational AI-ready pipelines and retrieval components and productionize at least one priority RAG or agent-grounding use case.
Qualifications
A bachelor’s degree and at least 6 years of overall relevant technical experience are required. Candidates need data, analytics or platform engineering experience, including at least 1–2 years building generative AI or AI/ML systems in production; experience with retrieval and semantic components for LLM or agent applications; strong Python and SQL; working knowledge of distributed processing; and hands-on experience with dbt, Fivetran and Snowflake in AWS environments. Experience with modern AI platforms or tooling, AI-consumable data products and complex, regulated enterprise environments is also sought. Preferred qualifications include a relevant degree concentration, master’s degree, cloud/data/AI certifications, financial-services experience and Palantir experience.
Compensation: Anticipated base salary is $160,000–$180,000, plus benefits; an annual discretionary incentive may also be available.