Associate, CLO Structuring (AI Implementation)

About Redding Ridge Asset Management

Redding Ridge Asset Management LLC (“Redding Ridge”) was established and seeded by Apollo Global Management, Inc. (together with its subsidiaries, “Apollo”) in 2016. Redding Ridge has assets under management of approximately $44.2 billion as of October 31, 2025. Redding Ridge’s primary business consists of acting as collateral manager for CLO transactions and related warehouse facilities in both the U.S. and Europe. Redding Ridge also provides structuring and ratings advisory services to issuers across various asset classes.

About the Opportunity

Redding Ridge Asset Management is a market-leading global CLO issuer, and we are building a team that will continue to define the future of CLOs and structured credit. As part of our Structuring & Advisory team, you will play a meaningful role across the full deal lifecycle — while helping to integrate advanced AI capabilities into how we structure, analyze, and execute transactions.

This role sits at the center of a high-performing platform with deep connectivity across the Apollo ecosystem, including MidCap Financial, Apollo Debt Solutions BDC, MFIC BDC, and Diameter Capital Partners — offering significant exposure and responsibility from day one.

Come help us grow the business and advance the next generation of structured finance while deploying the latest technology.

Primary Responsibilities

• Partner with senior structurers and the portfolio management team across the full deal lifecycle
• Pioneer AI integration into the CLO structuring process –  from automating routine analytics to developing intelligent models that sharpen deal insights and accelerate execution
• Evaluate and benchmark emerging AI tools and large language models for applicability across the CLO structuring lifecycle, providing informed recommendations on adoption and integration
• Formulate and clearly articulate recommendations on issuance strategy and equity deployment
• Design, train, and refine AI agents purpose-built for CLO structuring –  developing structured prompting frameworks, curating high-quality training datasets, and iterating on agent performance across cashflow modeling, rating agency scenario analysis, and covenant monitoring
• Lead investor model development in Intex Dealmaker and Excel, including replicating and stress-testing rating agency scenarios with precision
• Collaborate closely with Portfolio Management to monitor existing deals, proactively surface constraints, and drive solutions before they become problems
• Serve as a key liaison with legal counsel and rating agencies to ensure a smooth, on-time ratings process
• Maintain and enhance deal pipeline reporting for both internal and external audiences

Qualifications & Experience

• 2+ years of CLO structuring experience, with a strong command of deal mechanics and capital structure dynamics
• Proficient in Intex (Dealmaker and Calc) and Excel
• Familiar with AI agent frameworks, prompt engineering, and dataset curation methodologies; experience working with structured financial data in an AI/ML context is a strong plus
• Deeply engaged with AI and its applications in structured finance – with hands-on experience developing, prompting, or fine-tuning AI agents or LLM-based workflows; a demonstrated ability to bridge financial domain expertise with technical implementation
• Fluency across all rating agency methodologies, with hands-on experience navigating WARF, CDR, and OC/IC frameworks across deal types and market conditions
• A clear, compelling communicator (written and verbal) who can distill complexity into actionable insight for investors, counterparties, and internal stakeholders
• Exceptionally organized and detail-oriented, with the ability to manage multiple live deals simultaneously
• Bachelor's degree in Computer Science, Engineering, Finance, Accounting, Mathematics, or a related quantitative or technical field; advanced degrees or coursework in machine learning, data science, or AI are a plus

Pay Range

$175,000 - $200,000