Job Title – Senior AI Engineer Document Intelligence & LLM Infrastructure
Nature: Contract
Payroll: Vendor Payroll
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
Payout Type : Hourly
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
