AI Engineer

Davis AI; Paris; Sur place; EUR 90 000 - 140 000; Plein temps

Résumé du poste

Davis is seeking an engineer to own one of the core systems behind its real estate decision platform. You will turn raw, fragmented data into deliverables used by real estate professionals, building reliable, fast pipelines and operating multi-agent systems.

You will own system end to end: infra, orchestration, context engineering, data flow, and quality across outputs. Collaboration with clients and domain experts will sharpen constraints and drive dependable results.

Qualifications

• 1.5+ years building production software at scale.
• Experience building and evaluating LLM agents in real systems.
• Proven context engineering experience assembling the right context.
• Deep Python expertise with typing, async, and strong testing.
• Experience with databases and data modeling.
• Full-stack capability with backend emphasis.

Responsabilités

• Harness engineering: design and build the system layer around the model for reliable outputs at scale.
• Data ingestion & context assembly: manage messy, unstructured data from multiple sources.
• Storage & traceability: persist sources, extracted facts, and versions.
• Expert-in-the-loop UX: design review experiences with annotations, edits, and provenance.
• Evaluation & benchmarking: build an internal eval harness to track performance over time.

Connaissances

Production software

LLM agents

Context engineering

Python

Databases

Data modeling

Full-stack with backend emphasis

Lightweight agent frameworks

Description du poste

Davis is setting a new time standard for real estate development. We're targeting the $650B pre-construction market, where real estate developers today must coordinate with 4–5 fragmented stakeholders over weeks or months. In the future, they'll only need one: Davis.

We integrate every input that shapes a development decision into decision-ready outputs - investor-grade feasibility studies, investment analysis, and architect-certified designs delivered in days. Davis combines proprietary AI systems with expert review at every stage, ensuring velocity without compromising reliability.

Where We Operate

Early-stage development starts with a chain of high-stakes decisions, each requiring different data, different expertise, and different deliverables. Today, we deliver AI-powered outputs across the full spectrum - site sourcing, feasibility studies, architectural design, investment analysis, financial modeling, dataroom analysis, and more.

The Role

You will own one of the core systems behind what Davis delivers. Our agents take raw, fragmented data and turn it into deliverables that real estate professionals use to make high-stakes decisions. Your job is to make that pipeline reliable, fast, and indistinguishable from work done by the best human teams - then push it beyond what any human team could do.

You can expect to:

• Build and operate multi-agent systems that turn heterogeneous data into expert-grade deliverables across real estate development workflows.
• Own the system end to end: infra, orchestration, context engineering, how the system selects, structures, and injects the right information so agents behave reliably at scale.
• Ensure production-grade quality, performance, and reliability across every output we deliver to clients.
• Sit with clients and domain experts regularly to understand their constraints, challenge your own assumptions, and make sure every output meets and even exceeds clients' expectations.

Beyond the technical depth, this role will expose you to how real estate decisions are made, how clients think, and what it takes to deliver outputs they trust. You'll develop a sharp business intuition alongside your engineering skills.

Key Responsibilities

• Harness engineering: design and build the system layer around the model - context assembly, tool orchestration, verification, and report generation - to deliver consistent, high-quality outputs at scale.
• Data ingestion & context assembly: handle messy, unstructured project data from heterogeneous sources and ensure agents have the right context at all times.
• Storage & traceability: persist sources, extracted facts, intermediate results, report versions, and expert edits.
• Expert-in-the-loop UX: design and build the review experience (annotations, edits, approvals, diffs/version history, provenance display).
• Evaluation & benchmarking: build an internal eval harness (datasets, rubrics, regression tests, monitoring) to track agents performance over time.

What We're Looking For

• 1.5+ years building and deploying production software at scale (APIs, reliability, testing, performance).
• Experience building and evaluating LLM agents / multi-step workflows in real systems.
• Proven context engineering experience: you've built systems where reliability depends on assembling the right context (RAG over heterogeneous sources, summarization, conversation state, tool outputs).
• Deep Python expertise (clean architecture, typing, async/concurrency, strong testing culture).
• Strong experience with databases + data modeling (structured storage, document storage, versioning).
• Full-stack experience (you can ship a real UI), with a clear backend emphasis.
• Comfortable building from first principles: we don't want heavy agent frameworks — we prefer a lightweight, well-engineered codebase.

Nice to Have

• Experience operating LLM systems with observability and quality monitoring in production.
• Multi-country product experience (heterogeneous sources, localization, varying rules).
• NLP background

Why Join Us

You're joining a team at the very beginning - where every decision you make shapes the product, the culture, and the trajectory of the company. What you build here will be yours.

• Direct impact on growth: your work doesn't sit behind three layers of review. You ship, clients use it, and you see the results. Every output you improve translates directly into revenue and reputation.
• Real-world impact: your work supports investment decisions, accelerates development timelines, and helps redefine how cities are imagined, designed, and built.
• High ownership: own the full feasibility stack end to end, as the CEO of that part.
• Competitive salary and meaningful equity in an early-stage company.
• A small team with high standards - we ship fast and love working together.