Staff Full Stack Engineer

Now Hiring: Staff Full Stack Engineer (Architect Level)
LLM-Powered Document Intelligence Platform

📍 Mountain View, CA (Hybrid – 3 days onsite)
💼 Employment Type: c2c
🧠 Experience: 12+ years

We are seeking a Staff-level Full Stack Engineer (Architect) to lead the design and evolution of a production-grade LLM-powered Document Intelligence platform. This platform enables enterprises to reliably extract, interpret, validate, and operationalize data from complex, unstructured documents across regulated industries.

This role carries end-to-end architectural ownership, with a strong emphasis on scalability, correctness, explainability, and enterprise readiness.

Key Responsibilities

Architect and deliver scalable LLM pipelines for large, complex documents (100+ pages, PDFs, scanned files)

Design intelligent document ingestion using OCR combined with LLM-based extraction

Build semantic understanding layers leveraging embeddings and vector search (RAG architectures)

Implement human-in-the-loop workflows to support high-accuracy and compliance-driven use cases

Establish traceability and explainability frameworks, including source attribution and confidence scoring

Design and maintain secure, highly available backend services for enterprise document processing

Define architectural patterns to mitigate LLM hallucinations and clearly communicate model limitations

Partner with product, domain, and engineering teams to translate real-world workflows into robust software solutions

Technical Environment

Languages: Python (GenAI), Java

AI Architecture: Retrieval-Augmented Generation (RAG), embeddings, vector databases (Pinecone, Weaviate, or similar)

Data Pipelines: Large-scale ingestion, transformation, validation

Integrations: Structured outputs to Excel, enterprise platforms, and downstream APIs

Tooling: AI-assisted development tools (Cursor, Windsurf, etc.)

Qualifications

12+ years of experience building backend or full-stack systems at scale

Proven experience deploying LLM-based systems in production environments

Strong system design expertise with focus on security, observability, and reliability

Experience handling unstructured and semi-structured data (documents, forms, PDFs)

Ability to communicate AI behavior, confidence, and constraints to technical and non-technical stakeholders