AI/ML Engineer

Position Summary We are seeking an experienced AI/ML Engineer to join Carlyle’s Data Science team. In this role, you will design, build, and deploy advanced machine learning and generative AI solutions that support investment diligence, portfolio monitoring, and operational workflows. You will work at the intersection of large language models (LLMs), retrieval systems, financial data processing, and private equity analytics.

The ideal candidate combines strong software engineering and machine learning expertise with a curiosity about financial markets and investment processes. You will collaborate closely with data scientists, engineers, and investment professionals to bring AI capabilities into production and drive measurable business outcomes.

Responsibilities

AI Platform & Model Development

• Build and maintain Python-based services that parse, extract, and normalize financial statements, KPIs, and risk factors from PDFs, spreadsheets, and other diligence materials.
• Develop, fine-tune, and evaluate open-source and proprietary language models using historical diligence data, investment documentation, and structured financial datasets.
• Work with modern LLM provider APIs, agent frameworks, and orchestration platforms to build production-grade AI applications.
• Design and implement automated evaluation frameworks to measure model performance, factual accuracy, compliance adherence, and hallucination rates.
• Log experiments, model versions, and performance metrics within the firm’s MLOps platform to ensure reproducibility and governance.

Retrieval-Augmented AI & Data Engineering

• Design and deploy retrieval-augmented generation (RAG) pipelines integrated with virtual data rooms and internal knowledge repositories.
• Implement secure access controls, data governance standards, and document lineage tracking across AI workflows.
• Build scalable data ingestion, embedding, indexing, and retrieval systems that support high-quality AI outputs.
• Optimize model serving, inference pipelines, and retrieval architectures for performance, reliability, and cost efficiency.

Investment Workflow Integration

• Partner directly with investment professionals and deal teams throughout the diligence lifecycle.
• Translate AI-generated insights into inputs for valuation models, risk assessments, and investment memoranda.
• Collaborate with business stakeholders to identify opportunities where AI can improve investment decision-making and operational efficiency.
• Connect AI performance metrics (e.g., precision, recall, evaluation scores) to investment outcomes and private equity KPIs, including risk indicators and IRR sensitivity analyses.

Engineering & Operational Excellence

• Deploy and manage AI solutions in cloud environments using modern software engineering and DevOps practices.
• Contribute to coding standards, model governance, testing frameworks, and production monitoring.
• Stay current with advances in machine learning, generative AI, agentic systems, and financial technology applications.
• Mentor junior team members and contribute to a culture of innovation, collaboration, and continuous learning.

Requirements

Education & Certificates

• Bachelor’s degree, required
• Concentration in Computer Science, Data Science, Engineering, Mathematics, or a related quantitative field, preferred

Professional Experience

• 4+ years of professional experience designing, building, and deploying machine learning or AI systems in production environments.
• Interest in finance, investing, business analytics, or related domains; prior private equity experience preferred

Competencies & Attributes

• Knowledge of financial statements, corporate finance, valuation methodologies, or investment research workflows, preferred
• Hands-on experience working with LLM provider APIs, agent SDKs, and generative AI application frameworks.
• Strong programming skills in Python and experience with common ML libraries and frameworks, including PyTorch or TensorFlow, scikit-learn, and Hugging Face.
• Experience building data pipelines, model evaluation frameworks, and production AI services.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization technologies such as Docker.
• Understanding of software engineering best practices, including testing, CI/CD, version control, and observability.
• Excellent written and verbal communication skills, with t…