Senior AI Pipeline Engineer - Document Intelligence CLM

About Inhubber

Inhubber is a security-first AI-powered Contract Lifecycle Management (CLM) platform built for organizations with high compliance and data protection requirements. We combine end-to-end encryption and modern cloud architecture with advanced AI to extract analyze and generate contract intelligence.

Our platform processes sensitive legal documents for companies worldwide. We are now scaling our AI capabilities and looking for a senior engineer to take ownership of our production AI pipelines and help build the next generation of document intelligence.

The Role

We are looking for a hands-on Senior AI Pipeline Engineer (Python) who will:

1. Own and optimize our production extraction pipelines.

2. Deliver new document-analysis pipelines end-to-end.

3. Build the foundation for next-generation GenAI features (agentic contract drafting & interpretation).

This is a delivery-focused role . You will own the AI kernel (prompts evaluation logic structured outputs validation model/tool logic) while product engineers orchestrate workflows via stable APIs.

What Youll Do

1. Own & Extend Production Pipelines

• Maintain and optimize our Python-based extraction pipelines (AWS Lambda S3 Docker components).

• Ensure stable document processing and downstream triggering.

• Improve observability: logging metrics alerting traceability cost monitoring.

• Debug and stabilize real-world failure modes in production.

2. Deliver New Document Pipelines

• Design and implement end-to-end pipelines for new document families.

• Build evaluation datasets and regression tests.

• Prevent silent quality degradation through measurable metrics.

3. LLM-Based Interpretation & Structured Extraction

• Improve Q&A and structured extraction using LLMs.

• Implement structured outputs retrieval (RAG where useful) and deterministic validation.

• Add robust failure handling (timeouts retries fallbacks safe defaults).

4. GenAI Foundations

• Build agentic building blocks in Python behind stable APIs.

• Contribute to a contract-generation/editing kernel (planner drafter risk checks).

• Collaborate with backend/frontend teams for clean integration.

5. Production Readiness

• Ensure scalability cost-efficiency and security.

• Contribute to deployment/versioning/rollback strategies.

• Help define operational runbooks.

Must-Have Skills

• Strong production-grade Python (clean architecture testing packaging APIs).

• Experience owning code in production.

• AWS serverless (Lambda S3 required; Step Functions/SQS/CloudWatch a plus).

• Docker and containerized services.

• Proven experience maintaining/debugging automated pipelines.

• Hands-on experience with LLMs (OpenAI/Azure OpenAI/Anthropic or similar):

  • Structured outputs

  • Prompt iteration

  • Retrieval (RAG)

  • Evaluation approaches

Nice to Have

• Document AI experience (OCR layout extraction noisy PDFs).

• Evaluation-driven development (test sets regression checks quality metrics).

• Experience with cost/latency budgeting.

• Familiarity with TypeScript/Node.

• Experience integrating REST services.

First 90 Days What Success Looks Like

Weeks 12

• Fully understand current pipeline architecture.

• Stabilize staging/local environments.

• Define quality cost and latency baselines.

• Improve logging and monitoring.

Weeks 36

• Deliver a new production-ready pipeline for a new document family.

• Implement evaluation datasets and regression checks.

• Deploy with monitoring and rollback strategy.

Weeks 712

• Improve Q&A/extraction accuracy measurably.

• Deliver a first version of a GenAI contract drafting kernel.

• Harden operations (cost controls retries fallbacks documentation).

Tech Environment

• Frontend: React (TypeScript)

• Backend: Java (JEE)

• AI Pipelines: Python (AWS Lambda) Dockerized OCR/NLP

• Storage: AWS S3

• LLMs: Azure-hosted LLM APIs

• Infrastructure: AWS Azure (hybrid)

Engagement Details

• Senior-level role

• Long-term collaboration preferred

• Strong ownership mindset required

• Experience in regulated/sensitive data environments is a strong plus