AI & Data Architect

The AI & Data Architect 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 teams while establishing shared architectural standards and platforms for data and AI. The AI & Data Architect is a senior technical leader and trusted advisor to the Head of Data Transformation, serving as Carlyle’s primary architectural authority at the intersection of enterprise data and AI.

This role is responsible for designing, evolving, and governing Carlyle’s data and AI architecture end to end. The AI & Data Architect will ensure that enterprise data is trusted, well‑governed, context‑rich, and readily usable by analysts, applications, LLMs, agents, generative AI products, and analytical copilots.

The role requires direct, hands‑on experience building generative AI systems and applying an AI‑forward lens to architectural decisions. This includes defining how enterprise data is retrieved by agents, how semantic layers support natural‑language analytics, and how lineage, governance, and controls extend to model inputs and outputs.

This is a senior individual‑contributor architecture role that translates Carlyle’s data and AI strategy into executable technical designs, bridging strategy, engineering execution, governance, and business value across a federated operating model.

What Success Looks Like

In the first 12 months, this role will help define Carlyle’s target‑state data and AI architecture, establish reusable patterns for retrieval and semantic access, strengthen governance for AI‑consumable data, and guide priority AI and data initiatives from architecture through execution.

AI‑Ready Data Foundations (≈35%)

Architect AI‑ready data foundations – semantic layers, contextual metadata, data contracts, and retrieval‑ready knowledge stores – that allow LLMs, agents, and generative AI applications to reason reliably over Carlyle’s data. Design and govern enterprise patterns for retrieval‑augmented generation (RAG), vector stores, embedding pipelines, chunking strategies, and grounding approaches for AI use cases across the firm. Define how agents and copilots discover, query, and act on enterprise data, including tool and function interfaces, query routing, and architectural guardrails. Partner with Data Science and AI Engineering teams on feature stores, evaluation environments, and reusable AI data products. Advance semantic modeling and context engineering to enable natural‑language analytics, conversational reporting, and AI‑driven insights for the business.

Enterprise Data Architecture & Modernization (≈30%)

Act as the senior technical authority for enterprise data and AI architecture, partnering closely with the Head of Data Transformation to shape and execute Carlyle’s combined data and AI strategy. Design and evolve Carlyle’s cloud‑native, AI‑ready data platform, supporting analytics, reporting, automation, and generative AI at enterprise scale. Define target‑state architectures for data ingestion, transformation, storage, semantic layers, retrieval, and consumption across federated domains, with AI readiness and governed consumption as first‑class design requirements. Establish and enforce architectural standards for scalability, performance, security, resiliency, and cost efficiency across both data and AI workloads.

Modern Data & AI Pipelines and Platforms (≈20%)

Architect and guide the implementation of modern data and AI pipelines using tools such as dbt, Fivetran, Apache Iceberg, Snowflake, and Databricks, alongside MLOps/LLMOps platforms, AI gateways, feature stores, and vector databases (e.g., MLflow, Databricks Vector Search, pgvector, Pinecone). Design ELT, streaming, and embedding/indexing pipelines that are testable, observable, and resilient. Define data modeling standards (analytical, dimensional, and semantic) that enable trusted self‑service analytics and reliable AI grounding. Partner with data engineering and AI engineering teams to ensure consistent application of patterns and reuse of shared platform capabilities.

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

Partner with Data Governance to define standards for data quality, metadata management, lineage, and stewardship, extended to cover model lineage, prompt and response logging, evaluations, and AI risk. Ensure architectural designs for both data and AI support regulatory and compliance requirements, as well as emerging AI governance expectations. Promote reuse, interoperability, and consistent definitions of critical enterprise data, and ensure AI systems consume that data with the same rigor as human users.

Technical Leadership & Mentorship (≈5%)

Serve as a senior technical mentor to data engineers, AI engineers, and analysts. Lead architectural design reviews and provide guidance on complex data and AI initiatives. Influence technical decisions across a matrixed, federated organization without direct authority.

Education & Certificates

Bachelor’s or master’s degree in computer science, data engineering, information systems, or a related field, required. Relevant certifications in cloud, data architecture, data management, analytics, or AI/ML, preferred.

Professional Experience

10+ years of experience in data architecture, data engineering, or enterprise analytics, with at least 2 years of direct, hands‑on experience architecting generative AI or AI/ML systems in production. Proven experience designing retrieval, grounding, and semantic layers for LLM‑ and agent‑based applications, including RAG architectures, vector stores, embedding strategies, 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), with the ability to evaluate equivalents. Palantir experience a plus. Proven experience designing and operating modern, cloud‑native data platforms, with hands‑on expertise in AWS‑based data ecosystems and modern analytics stacks. Track record of building data platforms whose primary consumers include AI systems, not only BI tools and human analysts. 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 a design, rather than whether AI can be added later. Fluency in current AI architectural patterns (agents, tool use, evaluations, guardrails, observability) and a clear point of view on where they apply. Deep technical fluency combined with strong business acumen, with the ability to translate strategy into executable architectural designs. Pragmatic, delivery‑oriented mindset with strong attention to data quality, AI trust, and long‑term sustainability; able to distinguish durable architectural decisions from AI hype. Collaborative, trusted partner to senior leaders and technical teams, with experience operating in high‑visibility transformational initiatives.

Benefits/Compensation

The compensation range for this role is specific to New York and takes into account a wide range of factors including but not limited to the skill sets required/preferred; prior experience and training; licenses and/or certifications. The anticipated base salary range for this role is $200,000 to $220,000. In addition to the base salary, the hired professional will enjoy a comprehensive benefits package spanning retirement benefits, health insurance, life insurance and disability, paid time off, paid holidays, family planning benefits and various wellness programs. Additionally, the hired professional may also be eligible to participate in an annual discretionary incentive program, the award of which will be dependent on various factors, including, without limitation, individual and organizational performance.