Staff Fullstack Engineer (Python/React)

TL;DR

Staff Fullstack Engineer (Python/React): Building a next-generation Cloud Demand Forecasting Tool to replace legacy spreadsheet processes with an accent on scalable platform architecture and automated demand forecasting. Focus on implementing complex workflow engines, predictive analytics for infrastructure capacity, and high-performance data pipelines.

Location: Remote in Brazil, Colombia, Mexico, or Chile. Must be able to work PST hours.

Company

hirify.global is a cloud technology firm developing a greenfield modernization effort for infrastructure capacity planning.

What you will do

• Architect and build a greenfield Cloud Demand Forecasting platform including web portal, RESTful APIs, and CLI tools.
• Implement configurable workflow engines with SLA tracking, automated routing, and multi-stage approval chains.
• Develop a demand normalization and validation layer to standardize hardware SKUs and validate submissions against real-time constraints.
• Create executive dashboards with variance tracking, exception queue management, and multi-format output.
• Build predictive analytics capabilities using time series analysis and ML-based demand prediction.
• Own security implementation including SSO integration, granular RBAC, and complete audit logging.

Requirements

• 7+ years of software engineering experience, with 3+ years building enterprise-grade internal tools or forecasting platforms.
• Deep expertise in full-stack development, building both backend services and frontend interfaces end-to-end.
• Proven track record of migrating legacy systems (spreadsheets, fragmented portals) to modern web applications.
• Experience designing complex stateful workflow engines and RESTful APIs with OpenAPI/Swagger.
• Strong data engineering skills, including ETL pipelines and integration with data lakes.
• Must be based in Brazil, Colombia, Mexico, or Chile and able to work PST hours.

Nice to have

• Experience in infrastructure capacity planning or supply chain forecasting domains.
• Familiarity with scikit-learn, pandas, and time series forecasting frameworks.
• Knowledge of deep learning architectures for time series, such as LSTM or Temporal Fusion Transformers.
• Experience with observability and audit logging pipelines using Splunk or Grafana.