Overview
On Site
Contract - W2
Skills
Market Analysis
Analytics
Leadership
Solution Architecture
SEC
Use Cases
Risk Analysis
Named-Entity Recognition (NER)
Semantic Search
Data Science
Machine Learning Operations (ML Ops)
Amazon SageMaker
Amazon Redshift
Orchestration
Docker
Kubernetes
Terraform
Emerging Technologies
Vector Databases
Financial Services
Asset Management
Capital Market
Research
Workflow
Natural Language Processing
PyTorch
TensorFlow
scikit-learn
Snow Flake Schema
SQL
Databricks
Apache Spark
Cloud Computing
Machine Learning (ML)
Amazon Web Services
Enterprise Architecture
Data Governance
Cyber Security
Finance
Communication
Private Equity
Artificial Intelligence
Job Details
Title: Ai Architect
Location: Washington DC
Duration: 3 months will extend depending on the project
Financial experience is a must have
Role Description
Position Summary As the AI Architect, you will play a foundational role in designing the next-generation platform that blends AI, machine learning, LLMs, and financial domain intelligence to augment Carlyle's research workflows across private equity, real assets, credit, and infrastructure. You'll lead the technical vision and design of a system that ingests structured and unstructured financial and market data, applies intelligent analytics, and enables personalized, explainable insights for deal teams, portfolio managers, and senior leadership. Key Responsibilities - Platform & Solution Architecture Lead end-to-end architecture for Carlyle's AI Investment Research Platform, aligning with enterprise architecture and security standards. Architect scalable data pipelines and AI services to handle ingestion, transformation, and enrichment of internal and external financial data sources (e.g., SEC filings, earnings transcripts, market news, proprietary deal data). Design modular components to support use cases like thematic search, smart summarization, investment memo generation, and risk analysis. Implement and optimize advanced NLP and LLM-based models for tasks such as document classification, entity recognition, semantic search, and RAG (retrieval-augmented generation). Partner with Data Science, TechOps, and Investment teams to ensure models are explainable, secure, and aligned with financial domain needs. Establish frameworks for continuous learning, versioning, monitoring, and governance using ML Ops best practices. Architect robust, cloud-native AI infrastructure using AWS (preferred), leveraging services like SageMaker, Bedrock, Lambda, Glue, and Redshift. Guide containerization and orchestration using Docker, Kubernetes, and Terraform to enable scalable deployments across environments. Serve as a strategic technical partner to senior investment professionals, product managers, and data teams. Lead architecture review sessions, drive buy-in for strategic decisions, and ensure alignment with Carlyle's digital transformation goals. Evaluate emerging technologies and AI vendors to assess applicability in Carlyle's ecosystem (e.g., vector databases, multimodal AI, FinLLMs).
Qualifications 10+ years of experience in AI/ML architecture or engineering, including at least 3 years leading design for enterprise-scale platforms. Proven experience applying AI to financial services, asset management, or capital markets. Deep understanding of investment research workflows is a strong plus. Technical expertise in: NLP and LLMs (e.g., GPT, Claude, Cohere, Llama, BloombergGPT) ML frameworks: PyTorch, TensorFlow, Scikit-learn Data platforms: Snowflake, SQL, Databricks, Apache Spark Vector stores and RAG architectures (e.g., FAISS, Pinecone, Weaviate) Hands-on experience with cloud-native ML architectures, preferably on AWS. Strong knowledge of enterprise architecture, data governance, and cybersecurity in a regulated financial environment. Excellent communication skills, including the ability to present to C-suite and investment committee stakeholders. Experience at a private equity, hedge fund, or institutional asset manager. Familiarity with ESG data, alternative data sources, or portfolio company insights. Experience designing AI copilots or analyst assistants using retrieval-based methods and fine-tuned LLMs.
Location: Washington DC
Duration: 3 months will extend depending on the project
Financial experience is a must have
Role Description
Position Summary As the AI Architect, you will play a foundational role in designing the next-generation platform that blends AI, machine learning, LLMs, and financial domain intelligence to augment Carlyle's research workflows across private equity, real assets, credit, and infrastructure. You'll lead the technical vision and design of a system that ingests structured and unstructured financial and market data, applies intelligent analytics, and enables personalized, explainable insights for deal teams, portfolio managers, and senior leadership. Key Responsibilities - Platform & Solution Architecture Lead end-to-end architecture for Carlyle's AI Investment Research Platform, aligning with enterprise architecture and security standards. Architect scalable data pipelines and AI services to handle ingestion, transformation, and enrichment of internal and external financial data sources (e.g., SEC filings, earnings transcripts, market news, proprietary deal data). Design modular components to support use cases like thematic search, smart summarization, investment memo generation, and risk analysis. Implement and optimize advanced NLP and LLM-based models for tasks such as document classification, entity recognition, semantic search, and RAG (retrieval-augmented generation). Partner with Data Science, TechOps, and Investment teams to ensure models are explainable, secure, and aligned with financial domain needs. Establish frameworks for continuous learning, versioning, monitoring, and governance using ML Ops best practices. Architect robust, cloud-native AI infrastructure using AWS (preferred), leveraging services like SageMaker, Bedrock, Lambda, Glue, and Redshift. Guide containerization and orchestration using Docker, Kubernetes, and Terraform to enable scalable deployments across environments. Serve as a strategic technical partner to senior investment professionals, product managers, and data teams. Lead architecture review sessions, drive buy-in for strategic decisions, and ensure alignment with Carlyle's digital transformation goals. Evaluate emerging technologies and AI vendors to assess applicability in Carlyle's ecosystem (e.g., vector databases, multimodal AI, FinLLMs).
Qualifications 10+ years of experience in AI/ML architecture or engineering, including at least 3 years leading design for enterprise-scale platforms. Proven experience applying AI to financial services, asset management, or capital markets. Deep understanding of investment research workflows is a strong plus. Technical expertise in: NLP and LLMs (e.g., GPT, Claude, Cohere, Llama, BloombergGPT) ML frameworks: PyTorch, TensorFlow, Scikit-learn Data platforms: Snowflake, SQL, Databricks, Apache Spark Vector stores and RAG architectures (e.g., FAISS, Pinecone, Weaviate) Hands-on experience with cloud-native ML architectures, preferably on AWS. Strong knowledge of enterprise architecture, data governance, and cybersecurity in a regulated financial environment. Excellent communication skills, including the ability to present to C-suite and investment committee stakeholders. Experience at a private equity, hedge fund, or institutional asset manager. Familiarity with ESG data, alternative data sources, or portfolio company insights. Experience designing AI copilots or analyst assistants using retrieval-based methods and fine-tuned LLMs.
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