AI Engineer (Forward Deployed)

Hiring for Smart Bricks — via Flexiple, the recruiting partner on this role.
About Company
Smart Bricks is the applied AI lab for real assets. It builds frontier AI systems and autonomous agents for the global real estate and AI infrastructure industries, so institutions can do in minutes what used to take months of manual work. Smart Bricks is backed by a16z Speedrun, Techstars, 500 Global, HBS Angels and others, supported by Balderton Capital, and was selected for the Palantir Startup Fellowship (Cohort 003). It works with institutional investors, fund managers and family offices across the UK, Middle East and US.
Job Roles & Responsibilities
Build agentic systems: multi-step agents, tool use and workflows for sourcing, underwriting, due diligence, IC memos and portfolio monitoring
Own the AI stack: RAG and document pipelines, model routing across frontier and open models, prompt and context engineering, structured outputs into Excel and IC formats
Build evals and observability for accuracy, cost, latency and reliability so every number traces back to source
Build reusable connectors, data pipelines and components that make each new deployment faster
Embed with clients (real estate PE funds, institutional investors, family offices) to learn their data, workflows and decisions
Partner with GTM on discovery and scoping calls, turn messy client problems into a technical plan, and help close and expand accounts
Prototype within days, iterate on client feedback and take solutions to production; own the technical relationship through rollout
Turn one-off client builds into platform features
Ideal Candidate Profile
3+ years shipping production software, ideally in Python and/or TypeScript
Deep hands-on experience building with LLMs in production: agents, RAG, tool use, evals and context engineering (LangGraph, LlamaIndex, MCP or similar)
Strong grasp of the AI model landscape and accuracy/cost/speed trade-offs; solid ML fundamentals, including knowing when not to use an LLM
A builder's track record: founding engineer, early startup hire, ex-founder/CTO, or a top engineering team where you owned outcomes end to end
Comfortable in front of clients and able to explain technical trade-offs to a fund partner
Cloud fundamentals (AWS, GCP or Azure), data pipelines, and awareness of enterprise security (data isolation, access controls)
Comfortable working across UK and GCC hours; available to join immediately
Strong plus: finance, real estate, private markets or consulting exposure; forward-deployed or solutions engineering experience; document-heavy AI (PDFs, spreadsheets, data rooms); quantitative degree
Originally posted on: 13 Oct 2026

Skills
Python, TypeScript, AWS, Azure, GCP, Excel, LLM, Sourcing, Due Diligence

Experience
3+ yrs

Work arrangement
Yes