Quality Assurance Engineer - Generative AI (3-7 yrs)

Role : Gen-Al QA Engineer

We are looking for a detail-oriented and experienced Gen-Al QA Engineer to join our team. This role will focus on ensuring the quality and reliability of our natural language processing (NLP) and large language model (LLM) systems, including fine-tuned models, retrieval-augmented generation (RAG) workflows, and enterprise Al integrations

Responsibilities :

- Develop and execute test plans and test cases for NLP and LLM systems, ensuring they meet quality and performance standards

- Apply a strong understanding of Al/ML model testing methodologies, including performance evaluation, error analysis, hallucination detection, and fairness/bias evaluation

- Design and implement test automation for AI/ML applications, ensuring repeatable and scalable validation processes

- Utilize Python and relevant testing frameworks (e.g., pytest, unittest, Selenium) to automate functional and integration tests

- Conduct adversarial testing to identify vulnerabilities and edge cases in Al models

- Perform system integration testing, ensuring seamless interaction between Al models and enterprise applications like Salesforce and SAP

- Validate Al-generated outputs for accuracy, consistency, and compliance with business logic

- Ensure compliance with data privacy regulations, security protocols, and ethical considerations in Al

- Collaborate with data scientists and engineers to identify and resolve issues, improve model quality, and refine testing workflows

- Document and communicate test results, defects, and recommendations to stakeholders

Requirements :

Experience : 3+ years in QA testing, preferably in AI/ML application

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field

- Strong understanding of NLP, LLMs, fine-tuning, and retrieval-augmented generation (RAG)

- Proficiency in Python and relevant testing frameworks (pytest, unittest, Selenium, Robot Framework)

- Experience with ML testing methodologies, including performance benchmarking and error analysis.