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)
CI/CD & Automation
- Develop automated testing, versioning, and deployment (Azure DevOps, GitHub Actions, Prefect/Airflow)
- 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