Data & AI Engineer

Join a global investment firm with $485 billion of assets under management, across 678 investment vehicles. One of the world’s largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia. Connecting people, ideas, and capital to fuel growth for companies and performance for investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Investing primarily across Global Private Equity and Global Credit and deep expertise across industries, markets, and geographies. The Data & AI Engineer is an experienced, hands-on engineer who turns 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 against architectural standards, contributes to their evolution through hands-on learning, and partners closely with data science, AI engineering, governance, and domain teams to deliver trusted, AI-consumable data at enterprise scale.

• 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.
• Hybrid schedule either in New York City or Washington, DC. Local residency required.
• We are unable to transfer a visa or sponsor for permanent residency. Please do not apply under these conditions.
• Compensation is a $170K-$190K salary plus bonus.

Responsibilities Data Pipelines & Retrieval Systems (?35%)

• Build and operate AI-ready data pipelines  embedding generation, chunking, indexing, and refresh workflows  that make enterprise data reliably retrievable by LLMs, agents, and generative AI applications.
• Implement retrieval-augmented generation (RAG) components, including vector store integrations, hybrid search, re-ranking, and grounding logic, against architectural patterns defined by the Senior AI & Data Architect.
• Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks.
• Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products.
• Contribute to semantic and context engineering work 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 model, partnering with domain data engineers to apply shared platform capabilities consistently.

Semantic Layer & Data Product Development (?20%)

• Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self-service analytics and reliable AI grounding.
• Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers.
• Collaborate with the Senior AI & Data Architect to refine and extend enterpris…