Senior AI Engineer Document Intelligence LLM Infrastructure USA Shift

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