Mar 2026 — Present
Remote
Lawstronaut
AI Engineer — Individual Contributor
- Building a legal document structuring pipeline that slices laws and long-form legal documents into their logical hierarchy — titles, chapters, sections, subsections, clauses — so retrieval and drafting operate on real legal units, not arbitrary fixed-size chunks.
- Preserving parent–child relationships across the extracted hierarchy so every component stays addressable by its exact position in the source document.
- Self-hosting open-weight LLMs on GPU with vLLM — continuous batching and paged attention — to run document-scale inference in-house, taking per-token API cost and third-party rate limits out of the pipeline's hot path.
- Engineering the pipeline for scale: batched GPU inference, async orchestration, and stage-wise processing (segment → summarize → extract keywords) so large documents flow through without blocking.
- Building a multi-agent quality-control system on LangGraph — specialised agents validate organizational records for completeness, consistency, and rule compliance, with the workflow modelled as an explicit state graph so every verdict is inspectable instead of one opaque LLM call.
PythonvLLMGPU InferenceLangGraphLangChainFastAPIAzure OpenAIAzure AI SearchAzure AI Foundry