Solutions Architect - AI

Job Information

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• Date Opened 11/10/2026
• Industry Technology
• Job Type Full time
• Certifications Cloud certification (AWS, Azure, or GCP)
• Years of Experience 10+
• City Indianapolis
• State/Province Indiana
• Country United States
• Zip/Postal Code 46201

About Us

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Founded in 2015, RADcube is a leading technology consulting and software development firm headquartered in Carmel, Indiana. The company specializes in transforming enterprise ideas into real-world innovations by leveraging emerging technologies such as Artificial Intelligence, Blockchain, and Cloud Computing.

With nearly a decade of industry experience, RADcube serves diverse sectors, including healthcare, finance, government, and manufacturing.

Their core service portfolio includes

Digital Transformation and strategy consulting.

Custom Software Development tailored to specific business needs.

Advanced Data Analytics and AI-driven platforms.

Cybersecurity and risk management.

Recognized for its innovation-led culture, RADcube operates RADlabs, an R&D hub focused on high-impact solutions like Responsible AI and Intelligent Automation. The firm is committed to a human-centric approach, ensuring cutting-edge technology delivers measurable business outcomes and long-term success for global clients.

The company’s commitment to innovation has earned significant industry honors:

2026 TechPoint Mira Awards Finalist: Named a finalist for Tech Company of the Year, recognizing high-growth pioneers that demonstrate extraordinary leadership.

Public Sector Excellence

Awarded the Utah NASPO Cloud & Software Solutions Contract, solidifying their role as a trusted partner for large-scale government digital initiatives and more.

Job Description -------------------

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 client's 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 client's 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 client's release calendar.
• Guide business analysts in turning ambiguous business asks into structured, testable questions.
• Run knowledge transfer sessions and produce handover documentation.

Requirements

Qualifications

• Bachelor's 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.