Software Testing Lead

Zoho; Dadri; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

Dailoqa is seeking a TestAutomation Lead to architect and implement robust testing frameworks for software and AI/ML systems. You will bridge QA with AI validation, ensuring automated testing is integrated into CI/CD pipelines, addressing model accuracy, GenAI outputs, and ethical AI compliance.

Responsibilities include building scalable automation for UI, APIs, and AI model endpoints, creating GenAI-specific test suites, and collaborating with data scientists on model quality.

Qualifications

• 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.
• Certifications: ISTQB Advanced, CAST, or equivalent.
• Experience with MLOps tools: MLflow, Kubeflow, TFX.
• Familiarity with vector databases (Pinecone, Milvus) and RAG workflows.

Responsibilities

• Architect scalable test automation frameworks for frontend, backend, and AI/ML endpoints.
• Build GenAI-specific test suites for validating prompts, LLMs, and vector search accuracy.
• Develop performance testing strategies for AI pipelines and model serving latency.
• Establish CI/CD–driven continuous testing with GitHub Actions, Jenkins, or GitLab CI.
• Embed shift-left testing in development workflows with unit and contract tests.

Job description

As a TestAutomation Lead at Dailoqa, you’ll architect and implement robust testingframeworks for both software and AI/ML systems. You’ll bridge the gap between traditionalQA and AI-specific validation, ensuring seamless integration of automatedtesting into CI/CD pipelines while addressing unique challenges like modelaccuracy, GenAI output validation, and ethical AI compliance.

KeyResponsibilities

• Design and implement scalabletest 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., modelinference 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 checksinto development workflows (e.g., unit tests, contract testing).
  
• Collaborate with datascientists to test AI/ML models for accuracy , fairness , stability , and bias mitigation using tools like TensorFlowModel Analysis or MLflow.
  
• Validate model drift and retraining pipelines toensure consistent performance in production.
  

QualityMetrics & Reporting

• Define and track KPIs.
  
• Defect leakage rate
  
• Automation ROI (time saved vs. maintenanceeffort)
  
• Model accuracy thresholds
  
• Report risks and quality trendsto stakeholders in sprint reviews.
  
• Drive adoption of AI-specifictesting tools (e.g., LangChain for LLM testing, Great Expectations fordata validation).
  

SoftSkills

• Strong problem-solving skillsfor balancing speed and quality in fast-paced AI development.
  
• Ability to communicatetechnical risks to non-technical stakeholders.
  
• Collaborative mindset to workwith cross-functional teams (data scientists, ML engineers, DevOps).
  

Requirements

TechnicalRequirements

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
  
• Certifications: ISTQB Advanced,CAST, or equivalent.
  
• Experience with MLOps tools: MLflow, Kubeflow, TFX
  
• Familiarity with vector databases (Pinecone, Milvus) and RAG workflows.
  
• Experience with API testing, UItesting, and automated pipelines
  
• Understanding of AI/ML model testing, output evaluation, and non-deterministicbehavior validation
  
• Experience with testing AI chatbots, LLM responses, prompt engineeringoutcomes, or AI fairness/bias
  
• Familiarity with MLOps pipelines and automated validation of model performancein production
  
• Exposureto Agile/Scrum methodology and tools like Azure Boards