Job Title – Senior AI Engineer Document Intelligence & LLM Infrastructure
Nature: Contract
Time Zone : US Shift
Working Hours : 5.30pm – 2.30am
Work Location :Remote
Exp : 6 to 8 +Years
Contract Duration : 6 Months
No of Position : 1
Role Overview
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.
Core Responsibilities
- 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
- 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
- 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
○ Monitor and optimize GPU utilization and cost per request
○ Own production reliability of LLM serving infrastructure
- Deterministic Validation & Control Systems
○ Design validation layers outside the LLM
○ Implement schema enforcement, rule engines, invariants, and rejection logic
○ Build automated exception routing without default human review
○ Ensure auditability and reproducibility of extraction results
○ Create measurable correctness guarantees for high-stakes use cases
- Production Engineering & Scale
○ Design Systems That Handle
- Large document volumes
- Concurrency
- Failure states
- Observability and monitoring
○ Build logging, tracing, and metrics around document processing pipelines
○ Collaborate with cross-functional teams to ship production-grade AI systems
Required Experience
○ 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
Strongly Preferred
○ Experience with layout-aware models (LayoutLM, DocFormer, vision-language models)
○ Experience optimizing GPU cost and inference performance
○ Experience in regulated domains (healthcare, finance, compliance)
○ Familiarity with document-heavy workflows such as loan processing, underwriting, or claims
