Senior AI Engineer Document Intelligence & LLM Infrastructure

We are hiring a senior individual contributor to own and scale large-scale, OCR-driven document intelligence systems powered by self-hosted LLMs.

This is a deeply hands-on engineering role focused on production systems that process long-form documents (200+ pages), extract structured data deterministically, and run on optimized GPU-backed inference infrastructure.

You will work closely with the AI leadership team but will independently own architecture, performance, and reliability of document processing pipelines.

1.⁠ ⁠Large-Scale Document Intelligence Pipelines

○ Design and build end-to-end pipelines for processing long-form, OCR-heavy documents

○ Own PDF ingestion, layout-aware parsing, and multi-page document assembly

○ Implement robust chunking, segmentation, and metadata tracking across long documents

○ Handle exception detection, retries, and deterministic failure handling

○ Optimize systems to reliably process 200+ page documents at scale

2.⁠ ⁠OCR & Structured Extraction Systems

○ Work with OCR engines (Tesseract, PaddleOCR, layout-aware models, vision-language models)

○ Build layout-aware extraction systems using bounding boxes and structural metadata

○ Implement deterministic schema validation and cross-field consistency checks

○ Reduce reliance on manual QA through rule-based validation layers

○ Ensure traceability from extracted field back to source span

3.⁠ ⁠Self-Hosted LLM Inference (Production Ownership)

○ Deploy and operate open-source LLMs using:

§ vLLM

§ Hugging Face TGI

§ GPU-backed serving stacks

○ Tune inference performance:

§ KV cache management

§ Batching

§ Context window control

§ Throughput vs latency trade-offs