Software Testing Lead

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.

Key Responsibilities

Test Automation Strategy & Framework Design

• 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).

Continuous Testing & CI/CD Integration

• 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

• Strong 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). behavior validation

• Experience with testing AI chatbots, LL​M responses, prompt engineering outcomes, or AI fairness/bias
• Familiarity with MLOps pipelines and automated validation of model performance
• Exposure to Agile/Scrum methodology and tools like Azure Boards