Azure AI Senior Test Enginner

Company Description

Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
 
By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
 
From prototype to real-world impact - be part of a global shift by doing work that matters.

Job Description

1. Azure Knowledge

• Azure Services: Familiarity with Azure Cognitive Services (e.g., Azure Vision, Speech, Language, Decision), Azure Machine Learning is crucial.
• Azure Storage Services: Experience with Azure Blob Storage, Azure Data Lake, Azure SQL Database, Cosmos DB, and Azure Table Storage.
• Azure Data Factory: Knowledge of data integration, ETL (Extract, Transform, Load) processes, and building data pipelines.
• Azure DevOps: Knowledge of Azure DevOps for CI/CD pipelines, version control, build automation, and deployment.
• Azure AI Integration: Understanding of how AI models and services integrate into Azure’s ecosystem, including monitoring, scaling, and managing workloads.

2. Testing Techniques & Tools

• Test Automation: Experience with automated testing frameworks like Selenium, Appium, or frameworks specific to AI models, like pytest or TestNG, for unit and integration tests.
• Performance Testing: Proficiency in performance testing tools (e.g., Apache JMeter, LoadRunner) to ensure AI models perform well under different conditions.
• Load and Stress Testing: Understanding how to simulate high-load conditions on AI systems to test scalability.
• Regression Testing: Ensuring that new updates or model changes don’t negatively impact the performance or functionality of the AI system.
• Exploratory Testing: Ability to explore and test AI systems under unusual or edge-case conditions.

3. AI & Machine Learning Knowledge

• Basic AI/ML Concepts: Understanding machine learning models, algorithms (e.g., decision trees, neural networks), and their evaluation metrics.
• Model Evaluation: Knowledge of AI model evaluation metrics (e.g., accuracy, precision, recall, F1 score, ROC/AUC) and how to assess the quality of AI models.
• Bias and Fairness Testing: Knowledge of ethical AI testing, such as detecting and mitigating bias in models and ensuring fairness and accountability.
• Model Validation: Experience in validating model outputs against real-world data and requirements.

4. Programming and Scripting Skills

• Programming Languages: Proficiency in Python, to write test scripts and automate tests, especially in AI-driven environments.
• Scripting for Data Validation: Ability to write scripts for data validation, transformation, and pre/post-processing of datasets used for training or testing AI models.
• APIs and RESTful Services: Experience with testing AI-related APIs (e.g., for model deployment or communication between Azure AI services) using tools like Postman or writing custom test scripts.
• SQL/NoSQL: Advanced SQL skills and some experience with NoSQL databases like Cosmos DB.

5. Data Handling & Validation

• Data Analysis: Experience with data wrangling, preprocessing, and feature engineering techniques to understand and work with datasets used for training AI models.
• Data Integrity: Ensuring that data used in AI training is accurate, clean, and free from inconsistencies or errors.
• Data Privacy and Security: Understanding the security protocols and privacy regulations related to the data used in AI applications, such as GDPR and HIPAA.

6. Cloud Computing and DevOps

• Cloud Infrastructure: Understanding Azure’s cloud architecture, including compute, storage, networking, and security services.
• CI/CD Pipelines: Experience with creating and managing CI/CD pipelines using Azure DevOps to automate model testing, deployment, and updates.
• Containerization: Familiarity with Docker and Kubernetes for containerizing and deploying AI models in the cloud.

7. Collaboration and Communication

• Cross-Functional Team Collaboration: Work closely with data scientists, engineers, and business stakeholders to understand AI requirements and quality expectations.
• Documentation: Strong documentation skills to write detailed test cases, bug reports, and test results for AI models and services.
• Agile/Scrum Methodology: Familiarity with agile methodologies and participating in sprints and sprint reviews.

8. AI Ethics and Compliance

• Ethical AI Principles: Understanding ethical principles in AI, including fairness, accountability, transparency, and explainability.

9. Soft Skills

• Problem-Solving: Strong analytical skills to identify issues and troubleshoot problems in AI systems.
• Attention to Detail: Meticulous attention to detail when reviewing models, datasets, and results to ensure quality.
• Adaptability: Ability to quickly learn new tools, technologies, and frameworks as the AI landscape evolves.

Additional Information

At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.