GIS Full Stack Engineer

Key Responsibilities

 Data Ingestion and Processing Pipeline: Build and maintain an automated pipeline

to ingest and process raw satellite data from sources like the Registry of Open Data on

AWS, storing the results in a data lake built on Amazon S3.

 AI Model Training & Integration Framework: Develop a framework in Python to

automate the machine learning lifecycle, including the training, validation, versioning,

and storage of models for satellite image classification (Segmentation) and thermal

risk prediction (using libraries like XGBoost). You will also integrate the UTCI

(Universal Thermal Climate Index) calculation logic using the pythermalcomfort

library.

 Real-time Prediction API: Design and build a low-latency REST API using AWS

Lambda and API Gateway to serve real-time predictions from the AI models.

 Frontend Core Feature Implementation: Implement the core features of the

interactive dashboard using React and Mapbox GL JS, focusing on the intuitive

visualisation of complex geospatial heatmap data.

 Cloud Infrastructure: Design and construct the entire cloud infrastructure using

Infrastructure as Code (IaC) principles with the AWS CDK, ensuring the platform is

secure, cost-effective, and scalable.

Qualifications

Required Qualifications:

 A minimum of 3 years of professional software development experience

 Deep expertise in either Python-based backend development or React-based frontend

development

 Essential, in-depth experience designing, building, and deploying services on

Amazon Web Services (AWS), with a strong command of Lambda, S3, API

Gateway, and IAM

 A thorough understanding of REST principles and proven experience in designing

and building APIs from scratch.

 Proficiency in containerising applications using Docker

 Demonstrated ability to independently solve complex, open-ended technical

challenges

 Strong communication skills to articulate technical decisions and collaborate with the

team

Preferred Qualifications:

 Experience building complex, interactive mapping and data visualisation services

with Mapbox GL JS or similar libraries.

 Experience with GIS data processing using libraries like GeoPandas and Rasterio, or

familiarity with Google Earth Engine.

 Experience with ML model serving (MLOps) using frameworks like XGBoost and

Scikit-learn.

 Proven experience with Infrastructure as Code (IaC) tools such as AWS CDK or

Terraform.

 Previous experience in an early-stage start-up, demonstrating the ability to build a

• product from version 0 to 1.