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About the role
- 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
- 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
- Deploy and operate open-source LLMs using: vLLM Hugging Face TGI
- Monitor and optimize GPU utilization and cost per request
- Design validation layers outside the LLM
- Create measurable correctness guarantees for high-stakes use cases
- Collaborate with cross-functional teams to ship production-grade AI systems
Requirements
- 6+ years of hands-on Python engineering
- Proven production experience building OCR-driven document pipelines
- Experience handling long-form PDFs (100+ pages)
- Strong experience with: vLLM or Hugging Face TGI GPU-based LLM serving
- Open-source LLMs (LLaMA, Qwen, Mistral, etc.)
- Experience building deterministic validation systems (schema + rule enforcement)
- Strong debugging and systems-level thinking
- Ability to clearly articulate system trade-offs and business impact
Benefits
- Remote Job
- Full Time (40 hrs per week) - 5.30pm to 2.30am
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonOCRdocument pipelineschunkingsegmentationmetadata trackingschema validationdeterministic validation systemsdebuggingsystems-level thinking
Soft Skills
collaborationcommunicationarticulation of system trade-offsbusiness impact analysis