Description:
Modo Energy is building AI-powered tools for understanding global energy markets — combining large-scale data, forecasting models, and LLM systems into high-performance interfaces engineers and analysts use every day.
We're hiring a backend Python Engineer to join the Modelling team — the brains behind Modo Energy's forecast product. Our forecasts help investors, operators, and traders understand what energy assets will do next, and they need to be fast, reliable, and trusted.
You'll work across the full backend stack that powers these models: the APIs that serve forecasts to the Terminal, the pipelines that run them at scale, and the infrastructure that keeps everything ticking. You'll work closely with energy analysts and data scientists to turn quantitative models into production-grade systems that customers depend on daily.
We’re an AI-native engineering team — everyone uses AI coding tools and many of the systems we build are designed to be consumed by AI agents as much as humans.
What You'll Do
- Build and maintain the backend services that run and serve Modo's energy forecasting models — Django REST Framework APIs, Celery task pipelines, and the data layers that connect them
- Work directly with data scientists and energy analysts to take new models to production, making sure they run reliably and return results customers can trust
- Design and optimise job orchestration for compute-intensive modelling workloads — scheduling, retries, monitoring, and scaling via Celery, AWS, and Airflow
- Own AWS infrastructure and deployment across the full lifecycle — local development through to production — using Terraform and Docker
- Build monitoring, alerting, and validation tooling to catch model failures and data quality issues before customers do
- Own your projects end-to-end, from architecture and design through the full development lifecycle, deploying into a live production environment.
The Essentials What We're Looking For
- Bachelor’s/Master’s degree in Information Technology, Computer Science, or equivalent experience.
- 3–5 years of Python with strong backend fundamentals — clean, well-structured code, comfortable owning services end-to-end from API design through to deployment and monitoring
- Solid experience with Django REST Framework in production
- Proficiency with Celery for task orchestration and background processing
- Production experience with Docker, AWS, and infrastructure-as-code (Terraform)
- Strong testing habits — pytest, fixtures, mocking, CI pipelines that actually catch things
- Expert-level use of AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) — knowing when to trust them, when to intervene, and how to get the most out of them
- Good taste and judgment — you'll be making constant decisions about how to structure data pipelines, what to optimise, and when something is ready to ship