Manager, Product Development (For Quality Assurance)

Duties & Responsibilities

• Lead & Mentor a New Team: Hire, develop, and coach a cross‑functional product engineering QA team focused on agentic AI projects. Provide clear direction, remove blockers, and cultivate excellence with inclusive, growth‑mindset leadership.

• Full‑Stack Product Development & Quality Engineering: Own end‑to‑end validation across all layers of the stack. QA activities focused on establishing robust automation coverage using appropriate frameworks for .NET, Python, and TypeScript components, while implementing observability-driven testing based on logs, metrics, and traces. Through shift-left testing practices integrated into CI/CD pipelines, QA ensures scalability, reliability, and performance tuning are verified continuously, leading to stable and high-quality deployments.

• Requirements & Solution Design (ERP): Ensure functional and non-functional acceptance criteria are clearly defined, measurable, and testable. Validation of ERP workflows and integrations through simulation frameworks, ensuring traceability from requirement to release. Define QA process that verifies AI-native experiences with guardrails, human-in-the-loop validations, and complete auditability to meet ERP-grade reliability and compliance standards.

• AI Quality, Safety & Governance: Designing offline and online testing strategies for LLMs, RAG systems, and tool-use agents, measuring accuracy, hallucination rates, and safety compliance. Focus on defining and validating autonomy levels, fallback scenarios, and SLAs to ensure reliability and control within AI-driven features. Through risk-based testing and governance frameworks, QA helps ensure safe, compliant, and trustworthy AI behaviors.

• Data & Platform Stewardship: Ensure high-quality data and reliable platform integrations by validating data consistency, schema evolution, and pipeline robustness. Verification of data lineage, building reusable test datasets, and performing end-to-end validation across orchestration and connector layers. Maintain test environment parity with production to ensure reproducibility and accuracy of evaluation pipelines, directly supporting platform stability and reusability across ERPs.

• Enhancement & Customer Requests: Provide data-backed insights through defect trends, regression impact analysis, and risk assessment. Automation coverage ensures that enhancements do not introduce regressions, while QA validation confirms the integrity and functionality of customer-requested features.

• Trend Monitoring & Innovation: Drive experimentation with emerging AI-driven testing tools and self-healing automation frameworks. Contribute by evaluating agentic testing solutions, prompt evaluation tools, and generative testing frameworks that improve efficiency and coverage.

What you will likely bring:

• 10 to 15 years of experience in full-stack development and software engineering.

• Strong expertise in .NET, C#, Python, Typescript

• Proven track record in leading development teams and managing stakeholders effectively.

• Deep understanding of SDLC, software architecture, cloud platforms (Azure), and DevOps practices.

• Strong problem-solving, decision-making, and interpersonal skills.

• Experience with AI/ML technologies, LLMs, and NLP-based solutions is a plus

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Whatever your career journey, we’ll help you find the right path. Through our training courses, mentorship, and continuous support, you’ll get everything you need to thrive. At Epicor, your success is our success. And that success really matters, because we’re the essential partners for the world’s most essential businesses—the hardworking companies who make, move, and sell the things the world needs.

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