The Role
Lead Agentic AI quality efforts: define and execute testing strategies for LLMs, multi-agent and RAG systems; build automated evaluation pipelines, KPIs, dashboards and CI/CD quality gates; validate agent behavior, tool usage, integrations, and safety; hire and mentor QA engineers and report evaluation outcomes and governance to stakeholders.
Summary Generated by Built In
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.
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
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
Skills Required
- 7-12 years of experience in Software Testing, Quality Engineering, or Test Automation.
- Minimum 3+ years 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.
- Experience building automated evaluation pipelines, quality scoring mechanisms, dashboards, and CI/CD-integrated quality gates.
- Experience developing reusable test harnesses, simulators, and benchmarking frameworks for models, prompts, and agent configurations.
- 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.
- Strong communication, stakeholder management, analytical mindset, and leadership/mentoring capabilities.
- ISTQB, AI Testing, GenAI, Azure AI, AWS AI, or equivalent certifications.
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The Company
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
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