*** THIS JOB IS ONLY AVAILABLE TO INDIVIDUALS LIVING IN INDIA. NO RECRUITERS OR AGENCIES, PLEASE.***

About AssetIntel

AssetIntel is a software company that develops asset management software products for the transportation infrastructure industry. Our clients include state transportation agencies, city governments, toll authorities, transit agencies, the Federal Highway Administration (FHWA), and civil engineering firms. For more information, visit our website.

Job Description

We're looking for a Python developer to own the analytical engines behind our products — the codebases where math meets production software. These systems span a range of problems: long-term deterioration modeling and budget optimization, real-time hazard prediction using live data, probabilistic damage assessment, geospatial analysis, and lifecycle cost forecasting. They are the core of what makes our products work.

You'll work alongside domain experts and researchers to turn mathematical formulations into clean, maintainable, production-grade Python. You'll also progressively improve existing code — better architecture, better patterns, better reliability.

This is not a role where you can treat the math as a black box. You need to be genuinely curious about the models and algorithms you're implementing. You don't need to arrive knowing this domain, but you need to be the kind of person who wants to understand it deeply.

What You'll Do

• Translate mathematical specifications into production code. You'll receive formulations, equations, and pseudocode from the research side of the team. Your job is to understand the intent and write Python that is correct, readable, testable, and maintainable.
• Improve existing codebases. The current code works in production but has room for architectural improvement. You'll progressively modernize it — better modularity, type safety, test coverage, error handling, and overall code quality.
• Build new analytical features end-to-end. As our products evolve, you'll implement new capabilities — taking them from mathematical concept to deployed, production-ready code.
• Bridge the gap between research and engineering. The ability to communicate across these worlds — asking good questions, translating between math and code, pushing back constructively — will be critical to your success here.