{"id":443132,"url":"https://alion.io/job/modo-energy-python-engineer-terminalmodellingdata","title":"Python Engineer","company":{"id":172335,"name":"Modo Energy","domain":"modoenergy.com","url":"https://alion.io/company/modo-energy","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":89000,"max_usd":172000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":139},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Django","optional":false},{"name":"FastAPI","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Apache Kafka","optional":true},{"name":"RAG","optional":true},{"name":"Rest API","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-17T23:48:12Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-30T09:16:09Z","board_verified":true,"closed_at":null,"days_open":73,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":73},"description":"About Modo Energy\nThe energy transition is the biggest infrastructure buildout in human history. Modo Energy is the data platform at the centre of it.\nWe build the benchmarking, forecasting, and valuation tools that the world's most serious energy investors, developers, and operators depend on to make decisions. If a battery gets financed, built, or traded anywhere in the world, there's a good chance Modo Energy's data was in the room.\nFounded in 2019, we're 90+ people across London, New York, Sydney, and Madrid; $30M Series B, AI-native, and moving fast.\nThis is a rare chance to join a category-defining company at the moment it's scaling globally.\nThe Role\nModo Energy's engineering team builds the systems behind the Modo Energy Terminal, the data, research, and agentic platform that energy market professionals rely on every day. We're growing fast, and our ambitions are growing faster: from serving data and research to letting users (and their AI agents) query, forecast, and act on it directly.\nWe're hiring backend Python engineers to join one of our product teams: Terminal, Data, Ko (our AI Analyst), or Modelling. Each team owns a business capability end-to-end, including its own APIs, data pipelines, AWS infrastructure, and developer tooling. Wherever you land, you'll be building production systems from day one, with ownership over what you ship and how.\nWe're looking for engineers who learn fast and go wide. You won't be boxed into a single layer of the stack; the best people on our teams move fluidly between building a feature, hardening a pipeline, fixing an infra issue, and automating away the next one. If you're the kind of engineer who picks up a new tool, library, or part of the codebase without needing much of a runway, this is built for you.\nWhat You'll Do\nBuild and maintain backend services and APIs, on Django, FastAPI, or similar, that power a live product used daily by energy market professionals.\nDesign and operate data pipelines that ingest, transform, and serve energy market data reliably and at scale.\nOwn AWS infrastructure and deployment end-to-end using Terraform, from local development through to production.\nBuild the monitoring, observability, and alerting that let your team catch problems before customers do.\nSplit 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.\nDesign 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.\nWork directly with the people who understand the domain (energy analysts, data scientists, modellers) to make sure what you build is accurate and actually useful.\nContribute 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.\nCross 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.\nWhat We’re Looking For\nThe Essentials\n5+ 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.\nReal, hands-on experience with AWS and infrastructure-as-code (Terraform). You've run production infrastructure, not just experimented with it.\nProficiency with Django, FastAPI, or a comparable async Python framework.\nExperience with data pipelines and relational databases (PostgreSQL), and building systems that serve data reliably at scale.\nExpert-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.\nA 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.\nComfortable in a small, fast-moving team where you're expected to figure things out, make trade-offs, and ship, with real autonomy over how.\nNice to have\nExperience with LLM-powered applications: RAG pipelines, tool-calling architectures, or agentic systems.\nExperience designing external-facing APIs, including versioning, backwards compatibility, and OpenAPI/REST standards.\nFamiliarity with data infrastructure such as Airflow, Spark, Iceberg, or Kafka.\nExperience with energy markets.\nEqual Opportunity\nModo Energy is an equal opportunity employer. Employment decisions are made on the basis of qualifications, merit, and business need. We do not discriminate on the basis of race, colour, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, pregnancy, or any other characteristic protected by law.\nIf you require assistance or a reasonable accommodation during the application or interview process, please contact us at .\nWhat You Can Expect From Us\nAt Modo Energy, we believe that exceptional work deserves exceptional reward. We're a high-performance team; ambitious, collaborative, and genuinely motivated by the scale of what we're trying to build.\nYou'll have real ownership from day one, work alongside some of the brightest people in the industry, and be part of a company that's defining a new category in the global energy market. We're hybrid: everyone works Tuesday to Thursday in office, with Monday and Friday flexible. We offer top-of-market compensation, equity for every employee, and the space to take your career wherever you want it to go.\nWe're looking for people who want to do the best work of their careers. 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