AGENTIC AI CAREERS

Lead QA – Agentic AI

Principal Lead Engineer

9–15 Years Experience • AI Quality Leadership

Agentic AI LLM Testing AI Governance

Job Information

💼 Experience
9–15 Years
🎯 Role
Principal Lead Engineer
🤖 Domain
Agentic AI Testing
🧠 Focus
AI Strategy & Validation

Core Skills

Agentic AI LLM Validation AI Governance Prompt Evaluation Red Teaming Risk Assessment API Testing Python Robot Framework AWS Bedrock Claude LLM

AI Testing Areas

  • Agentic AI Validation
  • Autonomous Workflow Testing
  • Multi-Agent Collaboration Testing
  • Reasoning Chain Validation
  • Hallucination & Bias Detection
  • AI Governance & Compliance Testing
  • Performance & Scalability Testing
  • Risk-Based Release Validation

Key Responsibilities

  • Define end-to-end testing strategies for Agentic AI platforms.
  • Validate autonomous AI behaviors and reasoning workflows.
  • Lead hallucination, bias, ethics, and red-team testing.
  • Review AI test evidence and approve release readiness.
  • Partner with engineering and compliance teams on AI risk mitigation.
  • Mentor QA teams on advanced AI testing practices.

Required Skills

  • 9–15 years of QA and Quality Engineering experience.
  • Expertise in Agentic AI and autonomous systems testing.
  • Strong knowledge of LLM evaluation and prompt validation.
  • Experience with AI governance, guardrails, and model risk management.
  • Hands-on exposure to Python, Robot Framework, and API automation.
  • Ability to analyze AI logs, telemetry, and distributed agent architectures.

Preferred Skills

  • Experience with AWS Bedrock and Claude LLM.
  • Knowledge of CSA, GxP, or regulated AI validation.
  • Experience leading AI governance and compliance initiatives.
  • Ownership of enterprise automation frameworks and AI quality strategy.

Why This Role Matters

Agentic AI systems make autonomous decisions and execute complex workflows with minimal human intervention. This leadership role ensures these systems are accurate, reliable, explainable, compliant, and safe before they are deployed into production environments.


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