AI Engineer

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Product Engineering

Role Overview

As a Senior AI Engineer, you’ll design, deploy, and continuously improve the AI models that power Smart Bricks — from valuation engines and lead scoring to conversational agents and investment recommendations. You'll work at the intersection of machine learning, real estate data, and production systems, owning the full lifecycle from research and prototyping to scalable deployment.

You’ll collaborate closely with product managers, data scientists, and software engineers to build AI capabilities that are not only accurate, but useful, explainable, and deeply integrated into the user experience.

Key Responsibilities

Key Responsibilities

Design and build machine learning models for property valuation (AVMs), price forecasting, user intent prediction, and investment matching
Build, fine-tune, and deploy LLM-powered tools (e.g., smart agents, deal analysis assistants, chat interfaces)
Ingest, clean, and model structured and unstructured real estate data from multiple sources (DLD, Property Finder, UK Land Registry, etc.)
Collaborate with engineering to productionize models and deploy via APIs and real-time services
Conduct rigorous evaluation and iterate models based on real-world performance
Optimize for latency, explainability, scalability, and integration into product
Stay on top of the latest research and best practices in GenAI, recommender systems, and pricing engines
Mentor junior engineers and contribute to setting AI best practices across the company

Requirements & Qualifications

5+ years of experience in machine learning, data science, or AI engineering

Proven track record of deploying models in production environments
Strong knowledge of supervised learning, NLP, time series forecasting, and/or generative AI
Experience working with large-scale data pipelines and model monitoring
Familiarity with real estate, proptech, or fintech data is a strong plus
Ability to thrive in a fast-moving, ambiguous, startup-style environment
Excellent communication skills and ability to explain technical concepts to non-technical teams

Preferred Tech & Tools

Languages: Python (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, LangChain)

Data: PostgreSQL, BigQuery, Airflow, Redis, Snowflake
Infra: AWS, Docker, FastAPI, MLflow, Hugging Face, Ray
ML Types: Regression, XGBoost, LLM fine-tuning, embeddings, RAG pipelines, multi-modal models
Plus: Experience with production deployment of models in real-time environments

What we offer

Base salary plus commission
Tools and training for career growth
Monthly performance incentives
Collaborative and supportive sales culture
Health, dental, and vision benefits

Job-type

Full-time

Experience

All Levels

Salary

Competitive

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