AI and Data Engineer

Fund or Department Description

The Data & AI Engineer sits within 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.

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.

In the first 12 months, this role will deliver foundational AI‑ready data pipelines and retrieval components defined in the target‑state architecture, product ionize 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.

4 days per week

• 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.

• 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.

• 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 enterprise semantic standards based on what works in production.
• Support discovery and consumption tooling so that analysts,…