Artificial Intelligence Engineer

About the role

We are seeking an Artificial Intelligence Engineer to design and build the core data infrastructure that powers AI‑driven analysis for our real‑estate portfolio. The role bridges software engineering, data engineering and large‑language‑model integration to deliver actionable insights to non‑technical staff.

Key responsibilities

• Design and implement central data infrastructure (ClickHouse, data lake, etc.) aggregating data from property management, leasing, finance and operations.
• Integrate frontier LLMs (Claude, GPT‑4 or equivalents) via APIs to provide reasoning and analysis over internal data.
• Build and maintain retrieval pipelines that select, filter and deliver relevant data to LLMs within context window limits.
• Develop internal tools, dashboards or interfaces enabling non‑technical users to interact with AI‑powered analysis.
• Ensure data security, access controls and compliance for sensitive real‑estate and tenant information.
• Architect modular systems allowing easy swapping of LLM providers, data sources and tools.
• Monitor AI output quality, implement validation and feedback loops to mitigate hallucinations.
• Stay current with AI advancements and recommend new capabilities.
• Document architectures, processes and reduce bus‑factor risk.

Required profile

• 3–5 years of software engineering experience with strong Python proficiency.
• Hands‑on experience integrating LLM APIs (Anthropic, OpenAI or similar).
• Practical knowledge of data engineering, ETL pipelines and SQL on large datasets.
• Experience with analytical databases such as ClickHouse, PostgreSQL, BigQuery or Snowflake.
• Understanding of retrieval‑augmented generation, vector databases and context management.
• Familiarity with orchestration frameworks like LangChain or LlamaIndex.
• Ability to deploy and maintain production services using Docker and cloud infrastructure.
• Strong communication skills to explain technical constraints to non‑technical stakeholders.

Required skills

• Python
• LangChain
• ClickHouse
• Vector databases
• LLM APIs
• Context management strategies
• Snowflake
• LlamaIndex
• ETL pipelines
• BigQuery
• PostgreSQL
• SQL
• Docker
• Cloud Infrastructure