TEST AUTOMATION LEAD

Job Description

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

AI / ML Model Validation

Quality Metrics & Reporting

Soft Skills

Requirements

Technical Requirements

Must-Have

Requirements

Technical Requirements Must-Have 5–8 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