QA Engineer (C1) - Agentic AI

Key Responsibilities

• 1. Agentic AI Testing, Evaluation & Automation
• Define and execute testing strategies for LLM-based, multi-agent, RAG, and Agentic AI systems.
• Validate autonomous agent behavior, reasoning, memory, tool usage, API/database integrations, and end-to-end workflows.
• Evaluate AI outputs for accuracy, relevance, groundedness, consistency, completeness, toxicity, bias, hallucination risk, and guardrail compliance.
• Define AI quality KPIs such as hallucination rate, groundedness score, agent success rate, task completion rate, response relevancy, latency, cost efficiency, and user satisfaction.
• Build automated evaluation pipelines, quality scoring mechanisms, dashboards, and CI/CD-integrated quality gates.
• Develop reusable test harnesses, simulators, and benchmarking frameworks to compare models, prompts, and agent configurations.
• 2. Team Leadership & Capability Building
• Build and lead a team of Agentic AI Quality Engineers. 
• Define team structure, testing standards, best practices, and governance models. 
• Mentor QA engineers in AI testing methodologies, evaluation techniques, and automation frameworks. 
• Drive innovation and adoption of emerging AI testing tools and technologies. 
• Collaborate with Product, Engineering, Data Science, and AI Research teams to improve overall AI quality.
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• 3. Reporting & Stakeholder Management
• Provide quality assessments and recommendations to leadership and stakeholders. 
• Present testing outcomes, risk assessments, KPI trends, and model evaluation reports. 
• Drive quality governance for Agentic AI initiatives across the organization. 
• Ensure traceability of testing activities, evaluation criteria, and quality benchmarks.

Required Skills & Experience

Technical Skills

• 7–12 years of experience in Software Testing, Quality Engineering, or Test Automation.
• Minimum 3+ years of hands-on experience in GenAI, LLM Testing, Agentic AI Testing, or AI Quality Engineering.
• Strong understanding of LLMs, AI agents, RAG, prompt validation, tool calling, agent memory, MCP, and multi-agent orchestration.
• Experience defining AI quality metrics, evaluation methodologies, benchmarking frameworks, and model comparison approaches.
• Hands-on automation experience with Python, Playwright, Pytest, API automation, test framework development, and CI/CD quality gates.
• Experience with AI evaluation frameworks such as DeepEval, Ragas, LangSmith, OpenAI Evals, or equivalent tools.
• Exposure to cloud platforms such as Azure, AWS, or GCP.

Soft Skills

• Strong communication and stakeholder management skills.
• Analytical mindset with strong problem-solving ability.
• Self-driven, outcome-oriented, and capable of leading multiple initiatives in a fast-evolving AI ecosystem.

• Experience testing enterprise Agentic AI platforms and autonomous AI systems.
• Hands-on exposure to frameworks or tools such as LangGraph, CrewAI, AutoGen, Semantic Kernel, Microsoft Copilot Studio, TruLens, or Promptfoo.
• Exposure to AI observability, monitoring, model governance, responsible AI, and AI safety practices.
• Experience building AI quality dashboards and KPI reporting systems.
• ISTQB, AI Testing, GenAI, Azure AI, AWS AI, or equivalent certifications