Senior AI Engineer Document Intelligence & LLM Infrastructure USA Shift

 

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

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

○ Monitor and optimize GPU utilization and cost per request

○ Own production reliability of LLM serving infrastructure

 

4.⁠ ⁠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

 

5.⁠ ⁠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