Allegiance Real Estate — one of Dubai’s fastest-growing PropTech companies building an AI-powered platform to help agents and investors make faster, smarter real estate decisions — is hiring a Data & AI Infrastructure Engineer. This role owns the complete data and infrastructure layer end to end: designing ingestion pipelines for live market data, architecting a PostgreSQL + pgvector warehouse for 50+ international markets, building API integrations, transforming raw data into RAG-ready structured chunks, and monitoring pipeline health. You will work as a direct technical peer alongside the AI engineering lead.
About Allegiance Real Estate — AI PropTech, Dubai
Mission: Redefine modern real estate by combining innovation, technology, and exceptional customer experience through an AI-powered intelligence platform
Platform: Alpha — an AI-powered real estate platform serving agents and investors with data-driven, intelligent automation for faster, smarter decisions
Growth: One of Dubai’s fastest-growing real estate companies — expanding AI capabilities internationally with a multi-market franchise roadmap
Team: Small, senior, and deliberate — two senior technical hires building the AI brain of the company together, with direct access to leadership and fast decision-making
Ownership: Full ownership of architecture decisions — no layers of bureaucracy between your idea and shipping it in a live production environment
Technical Stack & Architecture
Database: PostgreSQL + pgvector — warehouse architecture for multi-market, multi-currency scaling (1 → 50+ markets)
Language: Python — pipeline development, ingestion, transformation, and API integration
Task Queues: Celery / Redis — queuing, caching, and workflow orchestration as data volume scales
APIs: Third-party data providers (pricing indices, transaction feeds, rental yield benchmarks) — multi-provider, multi-schema, multi-region
AI/RAG: Retrieval-Augmented Generation — transforming raw market data into structured chunks for embedding and retrieval
Position Overview
This Data & AI Infrastructure Engineer role at Allegiance Real Estate in Dubai is a high-ownership, senior technical position at the data and infrastructure layer of a live AI-powered real estate platform that is already in production and scaling internationally. You will design and build the ingestion pipelines for live market data across multiple regions — pricing indices, transaction feeds, rental yield benchmarks — architect the PostgreSQL + pgvector data warehouse to scale cleanly from 1 to 50+ markets, build the API integration layer connecting the Alpha platform to an expanding list of external data providers and internal tools, transform raw market data into structured chunks ready for RAG retrieval, build comprehensive monitoring and alerting for data pipeline health, own infrastructure scaling decisions around caching and queuing, and work as a direct technical peer to the AI engineering lead — two senior technical hires building the intelligence layer of an ambitious Dubai PropTech company together.
Why This Role Matters: As Data & AI Infrastructure Engineer at Allegiance Real Estate Dubai — owning the complete data and infrastructure layer of a live AI product that is already in production — you will make real architecture decisions from day one that determine what the entire AI platform is capable of, shape how real estate market data flows from 50+ international markets into an intelligent system that helps agents and investors make better property decisions in real time, work as a true technical equal alongside the AI engineering lead in a small senior team where your influence is direct, visible, and immediately impactful, and build a career at the genuinely exciting intersection of data engineering, AI infrastructure, and real estate technology in one of the world’s most dynamic property markets.
Key Responsibilities
Data Ingestion Pipeline Design & Architecture
- Design and build production-grade ingestion pipelines for live real estate market data sources across multiple international regions — including pricing indices, transaction feeds, rental yield benchmarks, and market intelligence data — each bringing its own provider, schema format, data freshness expectation, and authentication pattern
- Architect the complete data flow from source to model — not just moving data from A to B, but making considered architectural choices about ingestion frequency, transformation logic, error handling, retry strategies, and data validation that ensure the AI system always receives clean, current, and reliable inputs
- Handle the full complexity of multi-source data integration — normalising inconsistent schemas across providers, managing differing data quality levels, reconciling conflicting data points from overlapping sources, and building the transformation logic that turns raw, messy market data into structured, reliable information
- Own infrastructure decisions around scaling — determining when to introduce caching layers, message queuing via Celery and Redis, a proper ETL orchestration framework, or other architectural patterns as data volume, source count, and market coverage grow from current scale to the 50+ market international roadmap
PostgreSQL + pgvector Data Warehouse Architecture
- Architect the PostgreSQL data warehouse to scale cleanly from the current market footprint to 50+ international markets — designing the database schema, partitioning strategy, indexing approach, and data retention policies to handle the volume, variety, and velocity of multi-market real estate data without degrading query performance or data freshness
- Implement and optimise pgvector for the vector storage requirements of the RAG retrieval system — designing the vector table structures, embedding storage strategy, and query patterns that enable fast, accurate semantic retrieval of market data chunks by the AI layer
- Design for multi-currency and multi-region complexity — building the data models and transformation logic that correctly handle different currencies, property classification systems, measurement units, and data freshness requirements across markets with fundamentally different data availability and quality profiles
- Ensure warehouse performance and data freshness at scale — implementing appropriate caching strategies, materialised views, and background refresh jobs that keep AI-consumed data current without creating query bottlenecks or data consistency issues
API Integration Layer — External Providers & Internal Tools
- Build and maintain the API integration layer that connects the Alpha platform to a growing and evolving portfolio of external real estate data providers, internal tools, and third-party services — designing integrations for reliability at production scale, not just functionality in a development environment
- Implement robust API integration patterns — handling authentication (OAuth, API keys, JWT), rate limiting with intelligent backoff strategies, retry logic with appropriate jitter, circuit breaker patterns for unreliable providers, and data quality validation at the point of ingestion before bad data enters the warehouse
- Design the integration layer with extensibility as a first principle — building provider abstractions that make adding new data sources straightforward and isolated, so that the integration of a new market or data provider does not require rewriting core pipeline logic
- Monitor API integration health continuously — tracking provider uptime, response quality, rate limit consumption, and data freshness at the source level to enable proactive intervention before provider issues affect AI platform performance
RAG Data Transformation & AI Layer Collaboration
- Transform raw market data into structured, well-chunked content ready for RAG retrieval — partnering directly with the AI engineering lead to understand the embedding model’s input requirements, the retrieval system’s query patterns, and what specific data structures and chunk formats produce the most accurate and contextually relevant AI responses for real estate use cases
- Ensure that what gets embedded is accurate, current, and genuinely useful — not just technically ingested but validated for content quality, temporal freshness, and contextual completeness in a way that makes the RAG system more reliably accurate and commercially valuable
- Develop and maintain the transformation logic that converts raw real estate transaction and pricing data into semantically coherent, appropriately sized document chunks — optimising chunk boundaries, metadata tagging, and content structuring for the specific retrieval patterns that the Alpha platform’s AI features require
- Build the connective architecture that bridges the data infrastructure layer and the AI retrieval layer — working as a genuine technical peer to the AI engineer and making joint architectural decisions about where data transformation responsibilities lie, how embedding pipelines are triggered, and how data freshness signals propagate from the warehouse to the AI layer
Pipeline Monitoring, Alerting & Data Quality
- Build comprehensive monitoring and alerting systems for data pipeline health — tracking data freshness (time since last successful ingestion), data completeness (expected vs. actual records per market), schema drift (unexpected changes in provider data structures), and data quality signals that indicate upstream issues before they surface as AI model failures
- Design and implement alerts that ensure AI failures caused by bad data are caught at the infrastructure layer — before incorrect, stale, or incomplete data reaches the model and produces wrong answers for the agents and investors relying on the Alpha platform for real investment decisions
- Build observability tooling across the pipeline stack — dashboards, logs, and metrics that give the engineering team clear visibility of pipeline execution status, error rates, processing latency, and data warehouse health without requiring deep investigation of individual pipeline logs
What Allegiance Is Looking For
Required Experience
- Strong, demonstrable experience designing and operating production data pipelines — not just writing SQL queries, but architecting end-to-end ingestion, transformation, and storage systems that handle real data at scale, with real reliability requirements and real consequences for failure
- Hands-on PostgreSQL experience and ideally direct experience with pgvector or comparable vector-capable data stores — understanding both relational data modelling and the specific requirements of vector storage for embedding-based retrieval systems
- Real experience integrating multiple third-party APIs reliably — specifically including hands-on experience managing rate limits, multiple authentication schemes, retry logic, error handling, and data quality validation in a production integration environment
- Python proficiency for pipeline development — comfortable building clean, testable, maintainable Python code for data ingestion, transformation, and orchestration; familiarity with task queue frameworks (Celery, Redis) or ETL workflow orchestration tools is a significant advantage
- A working understanding of how AI and RAG systems consume data — you do not need to build the language models yourself, but you need to understand what constitutes good input for retrieval and embedding, and you need to care about data quality for the same reasons the AI engineer does
Preferred Attributes
- Experience designing for multi-region or multi-currency systems — understanding the data modelling, transformation, and validation challenges that arise when a single platform must handle fundamentally different data environments across international markets
- Comfort making real architecture decisions in a fast-moving startup environment — you will be making decisions early without a complete specification, and you need to be someone who finds that energising rather than unsettling
- A peer-to-peer technical collaboration style — you will work alongside the AI engineering lead as an equal, and the best outcome requires both of you to bring your full expertise to joint architectural decisions rather than one dictating to the other
About Allegiance Real Estate’s AI Platform in Dubai
Dubai’s real estate market is one of the world’s most dynamic, data-intensive, and internationally diverse property markets — with transaction volumes, price movement, and investor interest from across the globe creating a rich and complex data environment that is genuinely difficult to navigate without intelligent, data-driven tools. Allegiance Real Estate recognised this gap and set out to build the Alpha platform — an AI-powered intelligence system that helps real estate agents and investors move faster, decide smarter, and access market intelligence that was previously too fragmented, too delayed, or too opaque to act on confidently.
The Data & AI Infrastructure Engineer hire is the person who makes the AI possible at scale — building the reliable, clean, current data foundation that the AI system builds its intelligence on. In a small senior team, this person’s architecture decisions will shape what the platform can do, how fast it can scale, and how reliably it serves the agents and investors who depend on its outputs for real financial decisions. For data engineers who want genuine technical ownership, a high-calibre peer-to-peer engineering partnership, and the satisfaction of building something that is already in production and growing, this role at Allegiance Real Estate in Dubai is one of the most compelling opportunities in the UAE’s rapidly evolving AI and PropTech ecosystem right now.
Career Excellence: Join Allegiance Real Estate as Data & AI Infrastructure Engineer in Dubai — where your pipeline architecture skills, PostgreSQL and pgvector expertise, API integration capability, and RAG data engineering knowledge will power the AI intelligence layer of one of Dubai’s most ambitious and fastest-growing PropTech platforms.
Who Should Apply?
- Senior Data Engineers with AI Platform Experience: With proven production data pipeline design experience, PostgreSQL/pgvector expertise, and an understanding of how data infrastructure serves AI and RAG systems — ready for full ownership of a live AI product’s data layer in Dubai’s proptech sector
- Backend Engineers Pivoting to Data Infrastructure: With strong Python and API integration backgrounds and a genuine interest in data engineering at scale — comfortable making architecture decisions in a startup environment and excited to work directly alongside an AI engineering lead on a live product
- PropTech / Real Estate Technology Engineers: With experience building data infrastructure for property market platforms, transaction data systems, or real estate analytics products — bringing industry context that accelerates the understanding of the market data domain
- AI/MLOps-Adjacent Data Engineers: Who understand the data requirements of RAG systems, embedding pipelines, and retrieval architectures — and want to own the infrastructure layer that makes an AI platform reliable, accurate, and scalable rather than building the models themselves
- Dubai & UAE-Based Technical Engineers: Currently based in Dubai or ready to relocate — with the data engineering depth, API integration experience, and startup DNA to make an immediate and lasting impact on the architecture of an AI real estate platform that is already live and growing internationally
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