Data & AI Engineer

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 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.

In-office requirement: 4 days per week

Location: Washington, D.C. or New York, NYAI

Responsibilities

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, 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 enterprise semantic standards based on what works in production.

• Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction.

Data Quality, Observability & AI Trust (≈10%)

• Implement data quality checks, lineage capture, and pipeline observability across both data and AI workloads.

• Build logging, evaluation, and monitoring components for AI systems — including prompt and response capture, retrieval metrics, and model performance signals — in line with governance standards.

• Partner with Data Governance to operationalize metadata, stewardship, and access controls, ensuring AI systems consume enterprise data with the same rigor as human users.

• Surface issues early, propose remediations, and feed lessons learned back into architectural patterns.

Collaboration & Engineering Craft (≈5%)

• Participate in architectural design reviews and contribute hands-on engineering perspective to evolving patterns and standards.

• Mentor junior data engineers and analysts on modern data and AI engineering practices.

• Document patterns, write runbooks, and share knowledge across the federated organization to accelerate adoption of reusable platform capabilities.

Education & Certifications

• Bachelor’s degree, required

• Concentration in computer science, data engineering, information systems, or a related field, preferred

• Masters degree, preferred

• Relevant certifications in cloud, data engineering, analytics, or AI/ML are preferred

Professional Experience

-

• 6+ years of overall relevant technical experience, required

• Experience in data engineering, analytics engineering, or platform engineering, with at least 1-2 years of direct, hands-on experience building generative AI or AI/ML systems in production.

• Proven experience implementing retrieval, grounding, and semantic components for LLM- or agent-based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use.

• Hands-on experience with one or more modern AI platforms and tooling categories (e.g., AWS Bedrock, Databricks ML, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex or equivalents, MLflow, and vector databases such as Databricks Vector Search, pgvector, or Pinecone).

• Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark).

• Deep, hands-on experience with modern data stacks — dbt, Fivetran, Snowflake — in AWS-based environments.

• Track record of building data pipelines and products whose consumers include AI systems, not only BI tools and human analysts.

• Palantir experience a plus.

• Experience operating within federated data operating models and complex, regulated enterprise environments; financial services experience preferred.

Competencies & Attributes

• Demonstrated AI-forward instinct: defaults to asking how AI changes what gets built, rather than whether AI can be added later.

• Fluency in current AI engineering patterns (RAG, agents, tool use, evaluations, guardrails, observability) and the practical trade-offs involved in shipping them.

• Strong engineering craft: clean code, automated testing, thoughtful design, and a bias toward production-quality systems over prototypes.

• Pragmatic, delivery-oriented mindset with strong attention to data quality, AI trust, and long-term maintainability; able to distinguish durable engineering decisions from AI hype.

• Collaborative partner to architects, data scientists, AI engineers, and domain teams; comfortable operating in a matrixed, federated organization and in high-visibility transformational initiatives.

Benefits/Compensation

The compensation range for this role is specific to the applicable office location an