Modo Energy - Python Engineer (AI/Platform/Modelling/Data)
Requirements
• The Essentials • 3+ years of professional Python experience, with strong backend fundamentals. You write clean, well-structured code and are comfortable owning something end-to-end, from design through deployment and monitoring. • Real, hands-on experience with AWS and infrastructure-as-code (Terraform). You've run production infrastructure, not just experimented with it. • Proficiency with Django, FastAPI, or a comparable async Python framework. • Experience with data pipelines and relational databases (PostgreSQL), and building systems that serve data reliably at scale. • Expert-level use of AI coding tools (Cursor, Claude Code, GitHub Copilot, or similar). You know when to trust them, when to intervene, and how to get real leverage from them. We write a lot of code this way. • A fast learner with good judgement, comfortable making calls on system design and what's good enough to ship, and genuinely energized by picking up unfamiliar tools, stacks, or problem areas rather than staying in one lane. • Comfortable in a small, fast-moving team where you're expected to figure things out, make trade-offs, and ship, with real autonomy over how. • Experience with LLM-powered applications: RAG pipelines, tool-calling architectures, or agentic systems. • Experience designing external-facing APIs, including versioning, backwards compatibility, and OpenAPI/REST standards. • Familiarity with data infrastructure such as Airflow, Spark, Iceberg, or Kafka. • Experience with energy markets. • Equal Opportunity • Equal Opportunity
Responsibilities
• Build and maintain backend services and APIs, on Django, FastAPI, or similar, that power a live product used daily by energy market professionals. • Design and operate data pipelines that ingest, transform, and serve energy market data reliably and at scale. • Own AWS infrastructure and deployment end-to-end using Terraform, from local development through to production. • Build the monitoring, observability, and alerting that let your team catch problems before customers do. • Split your time between shipping new features and keeping the platform healthy. Maintenance, performance, and production issues are a normal, expected part of the job, not an interruption to it. • Design internal tools such as CLIs, APIs, and MCP servers that make your team and others faster, and that let both humans and AI agents work with our data safely and effectively. • Work directly with the people who understand the domain (energy analysts, data scientists, modellers) to make sure what you build is accurate and actually useful. • Contribute to the infrastructure behind our LLM-powered AI Analyst as needed: retrieval pipelines, tool-calling, and the plumbing that lets models work with real energy market data. • Cross into the parts of the stack outside your core comfort zone when the team needs it, whether that's a bit of frontend or a bit of deeper infra, and pick up what you don't already know.
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