QA Leadership
8–10 years in software QA, including at least 3 years in a Lead or Principal QA role.
Infor · Development
Quality Assurance, Senior (Gen AI and RAG)
Lead quality engineering for Infor AI Studio, validating agent architecture, retrieval-augmented generation, multi-agent coordination, and AI-driven enterprise workflows.
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Infor · AI Studio Quality Engineering
Role Overview
Lead QA for an Agentic AI Platform
Infor AI Studio is an agentic platform that enables teams to author, configure, and deploy Infor OS and ERP solutions using natural language processing capabilities.
This role leads the QA effort to validate AI Studio's agent architecture and its connected components, including ION, Data Fabric, Birst, EPM, RPA/IDP, CSD, and Portal. The focus is on quality strategy, evaluation frameworks, search relevance, multi-agent behaviour, and tenant isolation.
What You Will Do
Key Responsibilities
Lead the quality strategy, team execution, and release-readiness standards for AI Studio.
QA Strategy and Team Leadership
- Define and own the overall QA strategy for AI Studio, covering search relevance, multi-agent coordination, schema validation, tenant isolation, and LLM output quality.
- Lead a team of QA engineers, assign test coverage areas, review test plans, and ensure quality gates are met before each release.
- Translate new features, contribution model changes, and agent updates into test requirements in collaboration with architecture, component teams, and FDEs.
AI Evaluation and Search Quality
- Drive evaluation frameworks for non-deterministic Orchestrator outputs.
- Define correctness criteria for gap maps, dependency graphs, and blueprint quality across varying user inputs.
- Oversee search relevance regression testing to ensure that growth in the knowledge corpus does not degrade existing query-result quality.
Security, Metrics and Release Quality
- Define and enforce tenant isolation testing standards to prevent cross-tenant data leakage across sessions, project history, and search results.
- Report quality metrics to engineering leadership, including solution generation success rates, clarification round counts, agent failure rates, and corpus coverage gaps.
Basic Qualifications
Required Skills and Experience
The core technical and leadership capabilities specified for this position.
AI Product Ownership
Hands-on experience leading QA for an AI or data-intensive product in production.
RAG Testing
Search relevance, embedding quality, hybrid search behaviour, and corpus quality measurement.
Multi-Agent Testing
Distributed-system testing, inter-agent contracts, service-boundary tracing, and execution ordering.
Python Automation
Build and maintain automated test suites and define quality frameworks from scratch.
LLM Evaluation
RAGAS, DeepEval, or equivalent evaluation methodologies and tools.
API and Integration
REST API contract testing and integration testing across multi-service architectures.
Cloud Platforms
Experience with AWS (preferred), Azure, or GCP.
Stakeholder Communication
Translate quality findings into clear risk assessments for engineering leaders and product teams.
Education
Bachelor's Degree
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
Cloud Preference
AWS Preferred
Experience with at least one major cloud platform is required. AWS is identified as the preferred platform.
Preferred Qualifications
Additional Technical Expertise
Experience that can strengthen your contribution to AI Studio quality engineering.
Agentic AI and LLM Orchestration
- Experience leading QA for agentic AI systems or LLM orchestration pipelines where output correctness is semantic rather than deterministic.
- Knowledge of prompt adversarial testing to expose reasoning failures, hallucinations, incorrect component selection, or silent gap map errors.
Search Platforms and Observability
- Familiarity with OpenSearch, Elasticsearch, or other vector and hybrid search platforms, ideally with hands-on relevance test-suite design.
- Experience with MLOps or LLMOps observability tooling, including monitoring standards, alert thresholds, and trace inspection processes for AI pipelines.
SaaS Security and Enterprise Systems
- Familiarity with multi-tenant SaaS security testing, particularly tenant isolation and session boundary enforcement.
- Exposure to Infor OS, ERP systems, or enterprise workflow automation products.
- Experience establishing QA contribution processes for knowledge-driven systems where test data quality directly affects test coverage effectiveness.
CI/CD and DevOps
- Familiarity with CI/CD pipelines and DevOps practices, including integrating quality gates into automated deployment workflows.
Ideal Candidate
Who Should Apply?
A senior QA professional with 8–10 years of experience and at least 3 years in a Lead or Principal role, who has owned quality outcomes for a production AI or data-intensive product.
The strongest match will combine hands-on RAG testing, multi-agent system validation, Python automation, LLM evaluation, and the ability to lead a QA team while communicating product and engineering risks.
Platform Focus
Infor AI Studio
The role supports testing across AI Studio and connected enterprise components, including ION, Data Fabric, Birst, EPM, RPA/IDP, CSD, and Portal.
The initiative is positioned as a strategic effort to accelerate velocity suite sales, improve customer experience, and support collaboration across customers, services, FDEs, partners, and SCs.
About the Company
About Infor
Infor is a global provider of business cloud software for industry-specific markets. Its industry suites are designed to prioritize user experience, leverage data science, and integrate with existing systems.
More than 60,000 organizations worldwide rely on Infor to support business-wide digital transformation.
Learn more at Infor's official website.
Ready to Apply?
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