Senior AI Pipeline Engineer (Python / AWS / LLMs)

About Inhubber

Inhubber is building a security-first AI platform for contract intelligence. Our system processes sensitive legal documents for companies worldwide, combining end-to-end encryption, modern cloud infrastructure, and advanced AI document analysis. We are now scaling our AI document pipelines and GenAI capabilities.

The Role

We're looking for a Senior AI Pipeline Engineer who wants to own real production AI systems, not just experiments. You will take ownership of our AI document processing pipelines. Your work will power automated contract analysis, structured data extraction, legal document Q&A, and next-generation AI contract drafting. This is a hands-on engineering role, not research. You will build reliable AI pipelines that run in production at scale.

What You'll Work On

Production AI Pipelines

Maintain and improve our Python document processing pipelines running on AWS Lambda, S3, and Docker.

LLM-powered Extraction

Improve contract interpretation and structured extraction using LLMs, structured outputs, and retrieval pipelines.

New Document Pipelines

Design and ship new pipelines for additional document types with evaluation datasets and regression checks.

GenAI Systems

Help build the foundations for agentic contract drafting and interpretation systems.

Reliability & Operations

Improve observability, monitoring, cost control, and failure handling for real production workloads.

Our Tech Stack

• Python
• AWS Lambda
• Docker
• S3
• Azure LLM APIs
• React / TypeScript
• Java backend

You Should Have

• Strong production Python experience
• Experience owning real production systems
• AWS serverless experience (Lambda, S3)
• Docker / containerized services
• Experience with LLM pipelines

Ideally Also

• RAG pipelines
• Structured outputs
• Evaluation / test datasets
• Document AI (OCR / PDFs)

Why This Role Is Interesting

You will be working on one of the hardest real-world AI problems: turning unstructured legal documents into reliable structured intelligence. This requires solving challenges in document parsing, LLM reliability, evaluation systems, pipeline orchestration, and AI safety and validation.