AI & Data Architect
The Carlyle Group
Software Engineering, IT, Data Science
New York, NY, USA
Position Summary
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
Qualifications
- 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.
- 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.
- 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.
Company Information
The Carlyle Group (NASDAQ: CG) is a global investment firm with $477 billion of assets under management, across 678 investment vehicles as of December 31, 2025. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,500 professionals operating in 27 offices in North America, Europe, the Middle East, Asia and Australia.
Carlyle’s purpose is to connect 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. Carlyle invests across three segments – Global Private Equity, Global Credit and Carlyle AlpInvest – and has deep expertise across industries, markets, and geographies.
At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that, "To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives." Reflecting this view, emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.