AI Systems Engineer — Real Estate Feasibility & Multi-Agent

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 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.