AI Engineer

Founded in 1992, Cerberus is a global leader in alternative investing with approximately $71 billion in assets across complementary credit, private equity, and real estate strategies. We invest across the capital structure where our integrated investment platforms and proprietary operating capabilities create an edge to improve performance and drive long-term value. Our tenured teams have experience working collaboratively across asset classes, sectors, and geographies to seek strong risk-adjusted returns for our investors.

For more information about our people and platforms, visit us at

We are a new, but growing team of AI specialists - software engineers focused on AI development, and technology strategists - working to transform how an alternative investment firm with $65B in assets under management leverages technology and data. Our remit is broad, spanning investment operations, portfolio companies, and internal systems, giving the team the opportunity to shape the way the firm approaches analytics, automation, and decision-making.

We operate with the creativity and agility of a small team, tackling diverse, high-impact challenges across the firm. While we are embedded within a global investment platform, we maintain a collaborative, innovative culture where our AI talent can experiment, learn, and have real influence on business outcomes.

As a software engineer on the AI team at Cerberus, you’ll build internal applications that help our investment teams and portfolio companies use AI, data and automation in real workflows. This role is about taking high-value business problems from rough idea to working product, including full-stack apps, workflow automations, and AI-enabled tools.

You’ll design, implement and operate production-grade applications across the stack:
Python services, APIs, React and Type Script frontends, relational databases, SQL, orchestration layers, integrations with enterprise data sources, and LLM-powered agentic workflows.

We’re looking for engineers who care about impact: people who can prototype quickly, make pragmatic technical trade-offs, work closely with business and product stakeholders, and turn emerging AI capabilities into reliable products that users actually adopt.

• Build internal AI-enabled applications: Design and deliver full-stack applications that solve real business problems across investment workflows, portfolio operations, analytics, data management and process automation.
• Build on top of enterprise data and workflows: Develop applications and workflows that sit on top of structured business data, internal systems and third-party tools, whether that is Microsoft Fabric, Rayfin, relational databases, APIs or other environments.
• Develop across the stack: Build backend services and APIs in Python using FastAPI or similar frameworks, and create user-facing workflows and interfaces using React and Type Script.
• Implement AI agents in code: Build and integrate AI agents for use cases such as analytics, data management, application building and business process automation, using LLM provider APIs such as OpenAI or Anthropic.
• Turn prototypes into products: Take rough ideas, user pain points and proof-of-concepts through rapid iteration, user testing and, where appropriate, production deployment.
• Orchestrate reliable workflows: Use orchestration patterns and tools such as Temporal or similar systems to make multi-step workflows reliable, observable and maintainable.
• Engineer for adoption and maintainability: Partner with investment teams, portfolio companies, product managers and business stakeholders to make applications useful, usable, secure and supportable.
• Make pragmatic trade-offs: Balance speed, quality, security,…