About Smart Bricks
Smart Bricks
is a Dubai-based proptech company building real estate intelligence products powered by high-quality data, analytics, and AI. Our systems process large-scale property listings, transactions, and market signals to generate insights, valuations, and search experiences.
Weβre looking for a
ML
who can build reliable pipelines, enforce data quality, and enable AI/ML teams with clean, versioned datasets.
Roles and Responsibilities
- Build and maintain
Automated Valuation Models (AVMs)
for real estate pricing
- Continuously improve models by incorporating new datasets and ensuring models remain accurate over time
- Develop predictive models for:: price projections, rental yield forecasts, ROI / appreciation trends, property scoring and ranking
- Design and tune
feature engineering pipelines
to improve model performance and confidence
- Identify data gaps and define what new data is needed to improve model accuracy (and work with data teams to acquire it)
- Validate, test, and benchmark models using strong evaluation practices
- Monitor model performance and drift (data drift + concept drift) and trigger retraining strategies
- Collaborate closely with backend + data engineering teams to productionize models
- Build model scoring services and assist with integration into real-time APIs
- Work with modern AI workflows including
Agentic AI / OpenAI Agents
to enhance automation and intelligence in internal systems
Required Skills & Experience
- 6+ years of experience in developing AL Models
Nice-to-Have Skills
- Experience with time-series forecasting models (Prophet, ARIMA, XGBoost forecasting)
- Experience with model monitoring + retraining pipelines
- Familiarity with ML tooling like MLflow, DVC, Weights & Biases
- Experience deploying ML models via APIs (FastAPI, Flask, Docker)
- Familiarity with geospatial datasets and location-based modeling
- Experience with LLM workflows, prompt engineering, and
Agentic AI frameworks
(OpenAI Agents, LangChain, etc.)
- Knowledge of ranking systems and scoring frameworks
What We Value
- Strong sense of ownership: you treat models like products, not experiments
- Obsession with model accuracy, reliability, and explainability
- Comfort working with messy real-world data
- Curiosity to improve datasets and uncover better predictive signals
- Ability to debug models and pipelines end-to-end (data β features β model β output)
What Success Looks Like
- AVM models that stay accurate and robust across new market conditions
- Improved feature sets and smarter dataset design over time
- Strong monitoring of model drift and confidence degradation
- Well-tested, reproducible ML pipelines with documented assumptions and metrics
