Backend Engineer

Backend Engineer · Backend Engineer - AI-Powered Real Estate Feasibility & Investment Intelligence Platform · Project OverviewWe are developing a large-scale AI-powered real estate feasibility and investment intelligence platform designed to automate and enhance land and developm ...

Job description

Backend Engineer
Backend Engineer - AI-Powered Real Estate Feasibility & Investment Intelligence Platform

Project Overview

We are developing a large-scale AI-powered real estate feasibility and investment intelligence platform designed to automate and enhance land and development opportunity evaluation.

The platform integrates multiple data layers, including:
Geospatial data (GIS layers, zoning maps, satellite imagery)
Real estate transaction and pricing data
Government regulations and urban planning documents
Financial modeling inputs (CAPEX, OPEX, ROI, IRR, NPV)
Historical feasibility studies
Demographic and demand indicators
The system will process structured and unstructured inputs to:
Recommend optimal land use (residential, commercial, hospitality, mixed-use)
Forecast investment performance metrics (ROI, IRR, NPV, payback)
Conduct scenario comparison and sensitivity analysis
Evaluate investment risk
Generate structured, investor-ready feasibility reports

The backend system will serve as the orchestration layer connecting AI models, data pipelines, dashboards, reporting modules, and user management infrastructure.

Role Overview
The Backend Engineer will design, implement, and maintain the server-side architecture of the platform.

This role is responsible for building secure, scalable, and high-performance APIs and data services that support AI-driven workflows and enterprise-level decision systems.

Key Responsibilities
System Architecture and API Design
Design and implement a scalable backend architecture using modern web frameworks.

Develop RESTful and/or GraphQL APIs to serve AI outputs, geospatial data, financial simulations, and reporting modules.

Design service orchestration between AI models and frontend dashboards.

Ensure modular and extensible architecture to support future SaaS expansion.

Data Layer Management
Design and manage relational databases for structured financial, regulatory, and project data.
Implement optimized schema designs for feasibility studies and scenario storage.
Build secure data ingestion pipelines from external APIs, GIS sources, and document processing services.
Implement version control mechanisms for feasibility cases and AI-generated outputs.
AI Integration
Integrate machine learning and NLP models into production-grade backend services.
Build inference endpoints for predictive models and NLP pipelines.
Implement asynchronous processing for heavy AI workloads.
Ensure proper error handling and logging for model services.
Security and Access Control
Implement secure authentication and authorization mechanisms (role-based access control).
Ensure encryption of sensitive data in transit and at rest.
Build audit trails for feasibility case modifications and user interactions.
Ensure compliance with enterprise-grade security best practices.

Performance and Scalability
Optimize API response times and query performance.
Implement caching strategies for high-frequency queries.
Design for horizontal scalability in cloud environments.
Ensure high availability and fault tolerance.
Reporting and Document Generation
Build backend modules for automated feasibility report generation (PDF/Excel exports).
Support structured executive summaries and AI-generated analysis outputs.
Integrate visualization-ready endpoints for KPI dashboards.
DevOps and Deployment
Collaborate in containerization and cloud deployment workflows.
Support CI/CD pipelines and automated testing processes.
Monitor system health and ensure reliability across environments.
Collaboration and Documentation
Work closely with AI engineers, frontend developers, and domain experts.
Translate business requirements into backend system architecture.
Maintain clear and structured technical documentation.

Required Qualifications
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
Minimum 4+ years of professional backend development experience.
Proven experience designing scalable backend architectures for production systems.
Strong understanding of API design principles and microservices architecture.
Experience integrating AI/ML services into backend systems.
Experience working with structured financial or enterprise data systems is highly desirable.
Technical Stack Expectations
Core Backend Technologies
Python (advanced proficiency)
Django and/or FastAPI
Experience with or Laravel is considered an advantage
RESTful API design and implementation
Database Systems
PostgreSQL (strongly preferred)
Experience with relational database design and optimization
Familiarity with Redis for caching
Experience with data warehousing tools (e.g., BigQuery, Snowflake) is a plus
AI Integration
Experience deploying and serving ML models via API endpoints
Familiarity with ML inference workflows
Understanding of asynchronous task queues (e.g., Celery, RabbitMQ)
Cloud and Infrastructure
Experience with cloud platforms (GCP, Azure, or AWS)
Docker and containerization
Kubernetes familiarity is an advantage
CI/CD pipeline experience (GitHub Actions or similar)
Security and Governance
OAuth2 / JWT authentication
Role-based access control systems
Secure API design
Logging, monitoring, and audit mechanisms
Preferred Additional Competencies
Experience working with GIS or geospatial datasets
Experience in fintech, real estate tech, or enterprise SaaS platforms
Familiarity with regulatory or compliance-driven environments
Experience building multi-tenant SaaS architectures

Contract duration of more than 6 months. with 40 hours per week.

Mandatory skills:
RESTful API, Python, Database