Software Testing Lead

Dailoqa; Dadri; On-site; INR 400,000 - 700,000; Full time

Job summary

Dailoqa in India 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 and integrate automated testing into CI/CD pipelines.

You will design frameworks for frontend, backend, and model-serving endpoints using Selenium, Playwright, Postman, or Python/Java solutions, and build GenAI-specific test suites to verify prompts and LLM interactions.

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."
• Experience with MLOps tools: MLflow, Kubeflow, TFX.
• Familiarity with vector databases (Pinecone, Milvus) and RAG workflows.
• 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.

Responsibilities

• Design scalable test automation frameworks for frontend (UI/UX), backend APIs, and AI/ML model-serving endpoints using 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 (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 (unit tests, contract testing).
• Collaborate with data scientists 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 to ensure consistent production performance.

Skills

Test automation

AI/ML validation

Soft skills

Education

ISTQB Advanced

CAST

Tools

Selenium

Playwright

Cypress

REST Assured

Locust

JMeter

MLflow

Kubeflow

TFX

LangChain

Great Expectations

TensorFlow Model Analysis

Pinecone

Milvus

Azure Boards

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