CIFC Asset Management operates a multi-asset credit platform built on Bloomberg, Java, C#, Python, and cloud infrastructure (Azure, AWS, GCP, Databricks). The tech stack and active projects reveal a data-intensive business: the company is scaling enterprise data pipelines, CLO pricing analytics, and surveillance tools while simultaneously tackling data consolidation and quality issues across portfolio management and capital markets workflows. Hiring velocity is accelerating across engineering, finance, and operations—a signal they're investing in automation and platform infrastructure to handle growing complexity in structured credit and direct lending.
Founded in 2005, CIFC Asset Management is a Miami-based alternative asset manager specializing in credit solutions: CLOs, corporate credit (senior loans and high-yield bonds), structured credit, and direct lending. The firm operates investment teams across Miami, New York, Radnor PA, and London, serving institutional clients through separately managed accounts and proprietary funds. The business requires deep technical capability: portfolio modeling, real-time CLO pricing, leveraged loan surveillance, and regulatory reporting. Current focus areas include new CLO issuance pipelines, portfolio trading automation, and enterprise data infrastructure to consolidate and govern credit analytics across business lines.
Bloomberg, Java, C#, Python, pandas, NumPy, Azure, AWS, GCP, Databricks, SQL Server, Azure DevOps, Kubernetes, and CapIQ. Stack spans trading, quantitative modeling, cloud data infrastructure, and identity management.
Portfolio trading automation, CLO pricing data pipelines, leveraged loan surveillance tools, new CLO origination workflows, and enterprise data platform consolidation. Projects reflect scaling challenges in data accuracy, cross-team workflows, and regulatory reporting.
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CIFC Asset Management's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.