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).

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