- Design and build Python backend services, REST APIs, data access layers, and background jobs for ingestion and long-running computations.
- Develop parsers that convert vendor files, CSVs, and binary exports into validated records, handling inconsistent headers, changing units, and malformed inputs.
- Implement traceable data transformations for capacity, efficiency, resistance, and cycle aggregates.
- Build React and TypeScript interfaces with interactive charts for large time-series datasets, cross-cell and cross-test overlays, and detail-preserving zoom.
- Integrate an AI assistant using LLM APIs, tool calling, and retrieval over platform data and documentation, with citations and evaluations.
- Work with test engineers, lab supervisors, and upstream data owners to clarify needs, scope deliverables, and explain technical trade-offs.
- Own technical decisions across the data model, API, and user experience, delivering features from discovery through production.
- Implement production observability with structured logging and request correlation, and support delivery through Docker and GitLab CI.
- Maintain tests, strict typing, reviewable changes, and accurate documentation.
- Support an incremental transition from existing tools without disrupting engineers’ daily workflows.