Job Summary

Role Overview: As a Test Automation Lead at Dailoqa, you'll architect and implement robust testing frameworks for both software and AI/ML systems. You'll bridge the gap between traditional QA and AI-specific validation, ensuring seamless integration of automated testing into CI/CD pipelines while addressing unique challenges like model accuracy, GenAI output validation, and ethical AI compliance.

Responsibilities

- Design and implement scalable test automation frameworks for frontend (UI/UX), backend APIs, and AI/ML model-serving endpoints using tools like Selenium, Playwright, Postman, or custom Python/Java solutions.

- Build GenAI-specific test suites for validating prompt outputs, LLM-based chat interfaces, RAG systems, and vector search accuracy.

- Develop performance testing strategies for AI pipelines (e.g., model inference latency, resource utilization).

- Establish and maintain continuous testing pipelines integrated with GitHub Actions, Jenkins, or GitLab CI/CD.

- Implement shift-left testing by embedding automated checks into development workflows (e.g., unit tests, contract testing).

- AI/ML Model Validation: Collaborate with data scientists to test AI/ML models for accuracy, fairness, stability, and bias mitigation using tools like TensorFlow Model Analysis or MLflow.

- Validate model drift and retraining pipelines to ensure consistent performance in production.

- Quality Metrics Reporting: Define and track KPIs. Test coverage (code, data, scenarios). Defect leakage rate. Automation ROI (time saved vs. maintenance effort). Model accuracy thresholds.

- Report risks and quality trends to stakeholders in sprint reviews.

- Drive adoption of AI-specific testing tools (e.g.,



LangChain for LLM testing, Great Expectations for data validation).

Soft Skills

- Robust problem-solving skills for balancing speed and quality in fast-paced AI development.

- Ability to communicate technical risks to non-technical stakeholders.

- Collaborative mindset to work with cross-functional teams (data scientists, ML engineers, DevOps).

Requirements

Technical Requirements

- Must-Have 10 years in test automation, with 2+ years validating AI/ML systems.

- Expertise in: Automation tools: Selenium, Playwright, Cypress, REST Assured, Locust/JMeter.

- CI/CD: Jenkins, GitHub Actions, GitLab.

- AI/ML testing: Model validation, drift detection, GenAI output evaluation.

- Languages: Python, Java, or JavaScript.

- Certifications: ISTQB Advanced, CAST, or equivalent.

- Experience with MLOps tools: MLflow, Kubeflow, TFX.

- Familiarity with vector databases (Pinecone, Milvus) and RAG workflows.

- Strong programming/scripting experience in JavaScript, Python, Java, or similar.

- Experience with API testing, UI testing, and automated pipelines.

- Understanding of AI/ML model testing, output evaluation, and non-deterministic behavior validation.

- Experience with testing AI chatbots, LLM responses, prompt engineering outcomes, or AI fairness/bias.

- Familiarity with MLOps pipelines and automated validation of model performance in production.

- Exposure to Agile/Scrum methodology and tools like Azure Boards.

Disclaimer: This job posting and location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Software Testing Lead (Noida)
🏢 Dailoqa Solutions India
📍 Noida