Job
Description
Role name: Automation Test Lead Years of exp : 5 - 8 yrs About Dailoqa Dailoqa’s mission is to bridge human expertise and artificial intelligence to solve the challenges facing financial services. Our founding team of 20+ international leaders, including former CIOs and senior industry experts, combines extensive technical expertise with decades of real-world experience to create tailored solutions that harness the power of combined intelligence. With a focus on Financial Services clients, we have deep expertise across Risk & Regulations, Retail & Institutional Banking, Capital Markets, and Wealth & Asset Management. Dailoqa has global reach in UK, Europe, Africa, India, ASEAN, and Australia. We integrate AI into business strategies to deliver tangible outcomes and set new standards for the financial services industry. Working at Dailoqa will be hard work, our environment is fluid and fast-moving and you'll be part of a community that values innovation, collaboration, and relentless curiosity. We’re looking at people who: Are proactive, curious adaptable, and patient Shape the company's vision and will have a direct impact on its success. Have the opportunity for fast career growth. Have the opportunity to participate in the upside of an ultra-growth venture. Have fun 🙂 Don’t apply if: You want to work on a single layer of the application. You prefer to work on well-defined problems. You need clear, pre-defined processes. You prefer a relaxed and slow paced environment. Role Overview As an Automation Test 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). 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 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). Show more Show less
