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AI Engineer - Assistant Capabilities

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Built Technologies
Full Time position
Listed on 2026-09-13
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 70000 - 110000 GBP Yearly GBP 70000.00 110000.00 YEAR
Job Description & How to Apply Below
Location: Greater London

About Build

Build is creating the agentic AI stack for the built world. We help institutional real estate teams automate complex development and acquisitions workflows so important projects can move from concept to completion faster, with less cost, delay, and operational drag.

Our customers include some of the largest built-world institutions: alternative asset investors, developers, infrastructure owners, energy companies, industrial operators, and public-sector partners. Their work shapes the physical world, but the workflows behind that work are still slow, fragmented, document-heavy, and dependent on expert coordination.

We believe the next generation of built-world software will not just organize work. It will help do the work. Agents will reason across documents, drawings, financial models, market data, approvals, constraints, and expert judgment. Human experts will stay in control, but they will operate with far more leverage.

We are backed by leading investors and operators, including executives from Blackstone and OpenAI, alongside top venture firms. We are building a generational company at the intersection of AI and the physical world.

About the role

We are looking for an AI engineer, product to build agentic workflows that customers use in production.

This is a hands‑on engineering role for someone who wants to turn ambiguous, high‑value real estate workflows into reliable product experiences. You will work directly with customers, domain experts, designers, and engineers to understand how work actually happens, then build AI‑powered systems that help experts move faster and make better decisions.

You should be excited by the messy middle between product engineering and applied AI: long‑running agents, context engineering, document intelligence, visual reasoning, workflow orchestration, human‑in‑the‑loop review, evals, observability, and product surfaces that make agent work understandable and trustworthy.

This is not a research role. It is also not a demo‑building role. You will ship production software, own real customer outcomes, and help define what great AI‑native product engineering looks like in the built world.

What you’ll do

  • Design and ship production AI workflows for real estate design, acquisitions, diligence, planning, and project execution.

  • Work directly with customers and internal experts to map complex workflows into software systems with clear inputs, outputs, edge cases, and success criteria.

  • Build agents that reason across leases, zoning documents, site plans, drawings, financial models, market comps, investment memos, permits, emails, and project history.

  • Own full‑stack product features end to end, from backend workflow logic to user‑facing review, approval, and collaboration surfaces.

  • Design context strategies that help agents use the right documents, prior decisions, domain constraints, tool outputs, and user intent at the right time.

  • Build retrieval, extraction, structured‑output, and tool‑calling flows that are robust enough for expert users.

  • Create evals for document understanding, grounded reasoning, workflow completion, visual QA, accuracy, latency, and customer usefulness.

  • Inspect traces, debug failures, improve prompts and workflows, and turn customer feedback into measurable system improvements.

  • Partner with the core AI and infrastructure team to improve agent reliability, observability, evaluation, and developer velocity.

  • Raise the product quality bar for AI systems that need to be inspectable, explainable, and trusted.

Example projects

  • Build an acquisition diligence workflow that reads leases, zoning documents, market comps, financial models, and investment memos, then produces a grounded risk summary with citations and expert review.

  • Build a visual reasoning…

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