Our client is a fast growing Property Tech AI company
About the role
They are seeking a versatile Data & AI Engineer to build, deploy & maintain end-to-end data pipelines for downstream Gen AI applications. You'll design data models and transformations, build scalable ETL/ELT workflows, while learning fast and working on the AI agent space.
Key Responsibilities
Data Modeling & Pipeline development
Automate data ingestion from diverse sources (Databases, APIs, files, Sharepoint/ document management tools, URLs). Most files are expected to be unstructured documents with different file formats, tables, charts, process flows, schedules, construction layouts/drawings, etc.
Own chunking strategy, embedding, indexing all unstructured & structured data for efficient retrieval by downstream RAG/agent systems
Build, test, and maintain robust ETL/ELT workflows using Spark (batch & streaming)
Define and implement logical/physical data models and schemas. Develop schema mapping and data dictionary artifacts for cross-system consistency
Gen AI Integration
Instrument data pipelines to surface real-time context into LLM prompts
Implement prompt engineering and RAG for varied workflows within the RE/Construction industry vertical
Observability & Governance
Implement monitoring, alerting, and logging (data quality, latency, errors)
Apply access controls and data privacy safeguards (e.g., Unity Catalog, IAM)
Maintain reproducible environments with infrastructure as code (Terraform, ARM templates)
Required Skills & Experience
5 years in Data Engineering or similar role, with at least 12-24 months of exposure to building pipelines for unstructured data extraction including document processing with OCR, cloud-native solutions and chunking, indexing etc. for downstream consumption by RAG/ Gen AI applications.
Proficiency in Python, dlt for ETL/ELT pipeline, duckDB or equivalent tools for analytical in-process analysis, dvc for managing large files efficiently.
Solid SQL skills and experience designing and scaling relational databases. Familiarity with non-relational column based databases is preferred.
Familiarity with Prefect is preferred or others (e.g. Azure Data Factory)
Proficiency with the Azure ecosystem. Should have worked on Azure services in production.
Familiarity with RAG indexing, chunking and storage across file types for efficient retrieval.
Strong Dev Ops/Git workflows and CI/CD (CircleCI / Azure DevOps)
Experience deploying ML artifacts using MLflow, Docker, or Kubernetes is good to have.
Bonus skillsets:
Experience with Computer vision based extraction or experience in building ML models for production
Knowledge of agentic AI system design - memory, tools, context, orchestration
Knowledge of data governance, privacy laws (GDPR) and enterprise security patterns
They are an early-stage startup, so you are expected to wear many hats, working with things out of your comfort zone, but with real and direct impact in production.
Why our client?
Fast-growing, revenue-generating proptech startup
Flat, no BS environment, high autonomy for the right talent
Steep learning opportunities in real world enterprise production use-cases
Our client is a fast growing Property Tech AI company
About the role
They are seeking a versatile Data & AI Engineer to build, deploy & maintain end-to-end data pipelines for downstream Gen AI applications. You'll design data models and transformations, build scalable ETL/ELT workflows, while learning fast and working on the AI agent space.
Key Responsibilities
Data Modeling & Pipeline development
Automate data ingestion from diverse sources (Databases, APIs, files, Sharepoint/ document management tools, URLs). Most files are expected to be unstructured documents with different file formats, tables, charts, process flows, schedules, construction layouts/drawings, etc.
Own chunking strategy, embedding, indexing all unstructured & structured data for efficient retrieval by downstream RAG/agent systems
Build, test, and maintain robust ETL/ELT workflows using Spark (batch & streaming)
Define and implement logical/physical data models and schemas. Develop schema mapping and data dictionary artifacts for cross-system consistency
Gen AI Integration
Instrument data pipelines to surface real-time context into LLM prompts
Implement prompt engineering and RAG for varied workflows within the RE/Construction industry vertical
Observability & Governance
Implement monitoring, alerting, and logging (data quality, latency, errors)
Apply access controls and data privacy safeguards (e.g., Unity Catalog, IAM)
Maintain reproducible environments with infrastructure as code (Terraform, ARM templates)
Required Skills & Experience
5 years in Data Engineering or similar role, with at least 12-24 months of exposure to building pipelines for unstructured data extraction including document processing with OCR, cloud-native solutions and chunking, indexing etc. for downstream consumption by RAG/ Gen AI applications.
Proficiency in Python, dlt for ETL/ELT pipeline, duckDB or equivalent tools for analytical in-process analysis, dvc for managing large files efficiently.
Solid SQL skills and experience designing and scaling relational databases. Familiarity with non-relational column based databases is preferred.
Familiarity with Prefect is preferred or others (e.g. Azure Data Factory)
Proficiency with the Azure ecosystem. Should have worked on Azure services in production.
Familiarity with RAG indexing, chunking and storage across file types for efficient retrieval.
Strong Dev Ops/Git workflows and CI/CD (CircleCI / Azure DevOps)
Experience deploying ML artifacts using MLflow, Docker, or Kubernetes is good to have.
Bonus skillsets:
Experience with Computer vision based extraction or experience in building ML models for production
Knowledge of agentic AI system design - memory, tools, context, orchestration
Knowledge of data governance, privacy laws (GDPR) and enterprise security patterns
They are an early-stage startup, so you are expected to wear many hats, working with things out of your comfort zone, but with real and direct impact in production.
Why our client?
Fast-growing, revenue-generating proptech startup
Flat, no BS environment, high autonomy for the right talent
Steep learning opportunities in real world enterprise production use-cases
