QA Engineer (C1) - Agentic AI

Job Description: 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.

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.

Responsibilities: 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.

Qualifications: 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