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