PBR Real Estate
Product Manager - Real Estate Credit
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A new AI Commercial Real Estate Credit underwriting platform is looking to add to their growing team with the hire of a Product Manager.
Their strategy: building agentic AI for Commercial Real Estate debt fund managers, automating the screening, market research, and underwriting that currently consume most of an analyst's time.
The platform runs on a continuously updated market intelligence layer for European CRE — leasing fundamentals, investment comparables, supply pipelines, and transaction evidence — so the market is already understood before a deal arrives. A multi-agent system runs intake, research, and credit screening end to end, producing institutional-grade outputs that test sponsor assumptions rather than confirm them.
It is built for Real Estate credit funds that need consistency, speed, and a defensible audit trail — from emerging managers building institutional infrastructure to established funds standardising underwriting across every analyst and deal.
The platform is in production, and initial clients are onboarding. The founding team pairs deep credit experience with senior engineering: a CEO who spent twenty years in European real estate credit, most recently as Partner and Head of European Real Estate Credit at Ares; a CTO and former Deloitte partner who led financial modelling across $9 billion of transactions and has spent the last three years entirely in applied AI; and a Head of Product with fifteen years in commercial real estate, formerly an Executive Director in Goldman Sachs' Special Situations Group.
The role
This is a new, full-time position at the centre of the company, working directly with the Head of Product and the founders.
The company's defensibility is not the AI models, but the configuration library: twenty years of market knowledge recorded in a form machines can execute. Asset class taxonomies, geography conventions, broker methodology maps, screening logic, comparable evidence schemas, output formats and evaluation criteria. Your job is to grow, maintain and quality-control that library, and to make the agents measurably better at credit work every week.
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Responsibilities include:
- Encode domain knowledge. Translate CRE credit expertise into the precise definitions and rules that drive the agents: asset class definitions, market data conventions (which broker's vacancy series covers which universe, what “prime yield” means in each context, when two data points must never be averaged), screening criteria and comparable evidence standards.
- Work with AI models daily. Write, test and refine the prompts that instruct the agents; run structured evaluations of their output; diagnose why an output missed the mark and fix the configuration or prompt that caused it.
- Quality assurance. Review agent-produced screening decks the way an investment committee member would: check numbers against source documents, challenge the market narrative, and catch what a credit committee would catch.
- Client configuration. Own the setup of new clients on the platform: screener definitions, house formats and market configurations. Onboarding is configuration driven and takes days to weeks rather than months.
- Product definition. Work with the Head of Product on the roadmap: translate the credit workflow into specifications engineering can build, and feed what you learn from clients and quality assurance back into the product.
Candidate Requirements:
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- 2 to 5 years in commercial real estate credit or a directly adjacent seat: CRE lending, real estate debt funds, debt advisory, or real estate focused investment banking or credit ratings work. Experience of underwriting transactions and written or reviewed screening papers and investment committee memos.
- Strong Excel modelling – Have built and stress-tested debt cashflow models and can scrutinize and rebuild other’s models: rent rolls, debt sizing, covenant tests, sensitivities.
- Possess a scientific mindset – Precision is critical – eg. over definitions, being alert to how data is constructed (survey universe, measurement basis, methodology changes between sources) and being unwilling to compromise on inaccuracies.
- Hands-on engagement with AI – an interest and involvement in AI, using tools like ChatGPT or Claude in your own work and an interest to learn more. A technical background is not required, but the ability to write a precise credit memo is essential.
- Excellent written English
Desirable
- Exposure to several asset classes (logistics, offices, living sectors, hotels) or several European markets
- Working familiarity with market data sources: broker research, CoStar, MSCI/RCA, Green Street or similar
- Any prior experience of prompt engineering, evaluating LLM output, or taxonomy and data-structure work
The Opportunity
- Competitive salary plus meaningful equity options
- A unique and critical role with a revenue-generating AI company that will promote the development of skills that the next decade of financial services will run on
- Daily work alongside a founding team with senior credentials in credit, product and engineering
- London based, hybrid working
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