Kore.ai is a pioneering force in enterprise AI transformation, empowering organizations through our comprehensive agentic AI platform.

With innovative offerings across "AI for Service," "AI for Work," and "AI for Process," we're enabling over 400+ Global 2000 companies to fundamentally reimagine their operations, customer experiences and employee productivity.

Our end-to-end platform enables enterprises to build, deploy, manage, monitor, and continuously improve agentic applications at scale.

We've automated over 1 billion interactions every year with voice and digital AI in customer service, and transformed employee experiences for tens of thousands of employees through productivity and AI-driven workflow automation.

Recognized as a leader by Gartner, Forrester, IDC, ISG, and Everest, Kore.ai has secured Series D funding of $150M, including strategic investment from NVIDIA to drive Enterprise AI innovation.

Founded in 2014 and headquartered in Florida, we maintain a global presence with offices in India, UK, Germany, Korea, and Japan.

Position Summary :

As a Lead Quality Development Engineer in the SearchAI team, you will architect and drive the development of next-generation QA automation tools and frameworks.

You will collaborate closely with product, engineering, and DevOps teams to build automation suites, quality pipelines, and reusable utilities that enhance release reliability and system performance.

You will also play a key role in supporting customer implementations and contributing to development when needed.

LOCATION : Hyderabad (Work from Office).

Responsibilities :

- Lead the design and implementation of QA tools, test frameworks, and end-to-end automation pipelines for SearchAI components.

- Architect test data generation utilities, synthetic query evaluators, and RAG accuracy validation frameworks.

- Collaborate with engineering and DevOps to integrate automated testing into CI/CD pipelines.

- Drive performance, scalability, and reliability benchmarking for ingestion, retrieval, and LLM integration flows.

- Mentor QA engineers and developers to follow quality-first engineering practices.

- Participate in customer implementation cycles, assisting in troubleshooting and validation of production scenarios.

- Contribute to the development of reusable backend modules and test harnesses to validate enterprise-grade RAG systems.

- Partner with the R&D team to bring automation capabilities for retrievers, rerankers, and enrichment flows.

Qualifications / Skills Required :

- 7+ years in software or QA automation engineering, with a strong development background.

- Proficiency in Python and Node.js for building automation utilities and frameworks.

- Hands-on experience with RAG systems, LLMs, and vector databases (e.g., OpenSearch,

Weaviate, Pinecone).

- Strong understanding of test orchestration, CI/CD integration, and containerised

environments (Docker/Kubernetes).

- Experience in performance, regression, and integration testing for large-scale distributed

systems.

- Exposure to benchmarking AI workloads and evaluating retrieval quality.

- Experience building QA tools for AI or search platforms.

- Knowledge of metadata-driven retrieval, hybrid search testing, and LLM prompt evaluation.

- Familiarity with frameworks like Playwright, PyTest, JMeter, or Postman for automation.

- Exposure to Elasticsearch/OpenSearch APIs and data validation pipelines.

Education Qualification :

- Bachelors in engineering or Masters in computer applications.

Why Join Us? :

At Kore.ai, you won't be maintaining quality for conventional softwareyou'll be defining what quality means for an entirely new category of platform technology that enables enterprise-scale agentic applications.

Your work will directly influence how the world's leading organizations build, deploy, and trust AI systems, establishing standards that could transform the industry.

Join us in building not just a better platform, but the frameworks that ensure enterprise agentic applications deliver on their transformative promise safely, effectively, and responsibly at scale.