Solutions Architect AI

Solutions Architect - AI

Location: Indianapolis IN

Position Summary

The Solutions Architect - AI owns the technical design of how enterprise business knowledge is captured structured and served to AI systems so they answer business questions accurately. The role designs the domain context model the context repository and the evaluation harness that measures AI accuracy before and after context is added. It also designs the agentic automation and playbook that letst RADcube onboard new data domains faster than the first. The architect sets technical direction for a team that includes a techno-functional architect a data and AI engineer a validation engineer and a business analyst and presents progress to client sponsors every week.

Key Responsibilities

Context Architecture and Domain Modeling

• Define the Domain Context Model: the context elements and minimum standards that make a data product "context complete."
  
• Design the domain ontology (business concepts relationships hierarchies synonyms) and map it to data products and the clients knowledge graph and semantic layer.
  
• Run a rapid baseline of existing metadata definitions and data classification (PII GxP-regulated internal public) and close gaps that block business context.
  
• Set up ontology ownership review and change processes so context stays current as the business changes.
  

Business Context Repository and Integration

• Architect the Business Context Repository in a client-approved environment starting simple and hardening it to client security and data standards.
  
• Integrate the repository with the clients context injection layer metadata registry catalogs and graph without rebuilding components the client already owns.
  
• Align access controls to dataset classification.
  
• Deliver source code configuration and runbooks to the client repository.
  

AI Evaluation and Accuracy

• Design the evaluation harness: ground-truth question and answer sets an evaluation runner that tests AI answers with and without domain context and grading by numeric match query-result comparison and rubric.
  
• Build the failure-mode taxonomy (hallucination stale data misread schema wrong join or aggregation missing business context persona mismatch) and the scoring rubric.
  
• Use Claude or another approved LLM to generate and score answers then iterate on context and re-test to measure improvement.
  
• Report before and after accuracy by dataset question type and failure mode and rate each data product as AI-ready or not with remediation.
  

Agentic Automation and Scale

• Design agentic workflows orchestration patterns and human-in-the-loop controls that draft review and refresh context at scale.
  
• Automate intake from existing sources (metadata registry catalogs workshop outputs BI reports usage logs) and feedback loops from the questions users ask.
  
• Build scheduled refresh and staleness flags so context does not drift.
  
• Author the domain onboarding playbook effort model and prioritized roadmap for onboarding additional domains across a large data estate.
  

Client Leadership and Knowledge Transfer

• Serve as the technical lead with client program leads data owners and sponsors; confirm scope success criteria and priorities.
  
• Lead weekly demonstrations and incremental releases aligned to the clients release calendar.
  
• Guide business analysts in turning ambiguous business asks into structured testable questions.
  
• Run knowledge transfer sessions and produce handover documentation.
  

Requirements

Qualifications

• Bachelors degree in computer science Data Science Information Systems or a related field or equivalent experience.
  
• 8 years in data architecture or solutions architecture including 3 years designing AI or LLM solutions for production use.
  
• Hands-on experience with retrieval-augmented generation context or prompt engineering and LLM evaluation (ground-truth sets automated grading rubric scoring).
  
• Experience designing ontologies semantic layers or knowledge graphs and working with business glossaries and metadata catalogs.
  
• Strong SQL and data modeling skills; able to verify join paths grain and aggregation logic.
  
• Proficiency in Python for evaluation of runners pipelines and automation.
  
• Experience with agentic frameworks and orchestration with human-in-the-loop review design.
  
• Working knowledge of data classification and governance for PII and regulated data.
  
• Clear communicator who can run weekly demos for senior sponsors and translate business questions into testable requirements.
  
• Preferred: Life sciences or pharmaceutical data experience including GxP-regulated data. Experience with Claude or other enterprise LLM platforms. Graph database experience (for example Neo4j or RDF/OWL). Cloud certification (AWS Azure or GCP). Active client system credentials for immediate mobilization.
  

Required Skills:

Qualifications Bachelors degree in computer science Data Science Information Systems or a related field or equivalent experience. 8 years in data architecture or solutions architecture including 3 years designing AI or LLM solutions for production use. Hands-on experience with retrieval-augmented generation context or prompt engineering and LLM evaluation (ground-truth sets automated grading rubric scoring). Experience designing ontologies semantic layers or knowledge graphs and working with business glossaries and metadata catalogs. Strong SQL and data modeling skills; able to verify join paths grain and aggregation logic. Proficiency in Python for evaluation of runners pipelines and automation. Experience with agentic frameworks and orchestration with human-in-the-loop review design. Working knowledge of data classification and governance for PII and regulated data. Clear communicator who can run weekly demos for senior sponsors and translate business questions into testable requirements. Preferred: Life sciences or pharmaceutical data experience including GxP-regulated data. Experience with Claude or other enterprise LLM platforms. Graph database experience (for example Neo4j or RDF/OWL). Cloud certification (AWS Azure or GCP). Active client system credentials for immediate mobilization.----