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Software Engineering AI Trainer (Ecuador) at Anyone AI. Remote Location: Ecuador - Fully Remote. Anyone AI is recruiting skilled software engineers to work as a project consultants. . Qualifications:. Advanced professional written proficiency in English. 3–7 years of professional software engineering experience. Strong proficiency in Python and JavaScript/TypeScript; working knowledge of Java, C#, or Go. Backend or full‑stack development experience in production systems. Experience with testing frameworks (e.g., pytest, Jest, JUnit, xUnit, Go testing). Proven ability to debug and navigate large, multi‑file codebases. Experience with code reviews, refactoring, and production migrations. Engagement:. Part-time, project-based expert evaluation work. Work Type:. Remote. Contributors will design and evaluate realistic software engineering tasks, including bug resolution, feature implementation, refactoring/migration, and test generation. Work includes both creating complex coding scenarios and reviewing peer submissions for quality and accuracy.. This is a project-based consultant role. Consultants will be paid on a per-project basis; hourly rates are estimates based on anticipated completion time. Consultants control their own schedule, provide their own tools, and may simultaneously provide services to other vendors/employers (subject to those vendors’ allowances).. Responsibilities:. Contributors will:. Design and implement multi-file coding tasks across bug fixing, feature development, refactoring, and testing. Write clear natural-language specifications and reference implementations. Develop and extend unit and integration test suites. Review peer-generated tasks for correctness, clarity, and realism. Identify edge cases, ambiguities, and potential failure modes. Ensure alignment between specifications, code, and expected outputs. Expected Outcomes:. High-quality, production-realistic coding tasks. Complete and correct reference implementations. Robust test coverage and validation artifacts. Structured, actionable peer review feedback