Staff AI Search Architect

Ironclad

United States

Remote

USD 180,000 - 240,000

Full time

3 days ago
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Benefits offered by this job

Dental, Vision & Health Insurance
401K
Wellness reimbursement
Flexible vacation policy
Parental leave
Work from home opportunities
Generous health benefits

Job summary

Ironclad is hiring for a Staff/Senior Staff Engineer on the Intelligence Platform team to own agentic search architecture, integrating LLMs with retrieval to answer complex contract questions. You will lead eval-driven development, set technical direction, and mentor engineers across AI teams.

You will partner with product and engineering leaders to raise search quality, drive architecture, and own content understanding pipelines that convert documents into usable structured data for retrieval

Qualifications

  • 10+ years building production systems in search/information retrieval.
  • Experience with LLMs, retrieval, and RAG architectures.
  • Proven ability to drive architectural decisions and technical direction.
  • Experience building eval-driven benchmarks and A/B workflows.

Responsibilities

  • Own agentic search architecture, combining LLMs and retrieval to answer complex questions.
  • Design and run benchmarks, experiments, and evaluation of search quality.
  • Raise the company-wide search quality standard and influence others.
  • Own content understanding and ingestion to feed retrieval systems.
  • Set technical direction across AI org teams and mentor engineers.

Skills

Agentic Search
LLMs
Retrieval systems
Eval-driven development
Architectural leadership
Mentoring engineers

Education

MS in CS
BS in CS

Tools

Elasticsearch
OpenSearch
Solr
Python

Job description

Ironclad is hiring for a Staff/Senior Staff Engineer on the Intelligence Platform team to own agentic search architecture, integrating LLMs with retrieval to answer complex contract questions. You will lead eval-driven development, set technical direction, and mentor engineers across AI teams.

You will partner with product and engineering leaders to raise search quality, drive architecture, and own content understanding pipelines that convert documents into usable structured data for retrieval

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