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AI Test Engineer- BU
Eli Lilly and Company
posted 1d ago
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Flexible timing
Key skills for the job
Job Description
AI Testing Strategy and Planning
- Collaborate with cross-functional teams to develop comprehensive AI testing strategies and plans for AI-powered applications.
- Work closely with product managers, data scientists, and developers to understand AI model requirements, use cases, and project goals.
- Define the scope and objectives of AI testing efforts, including performance, accuracy, bias detection, and robustness of AI models.
Test Execution for AI Models and Algorithms
- Design, develop, and execute test cases for AI systems and models (including machine learning and deep learning algorithms).
- Test and validate AI solutions across various stages of the development lifecycle, including model training, testing, and deployment.
- Ensure that AI models meet business requirements and perform accurately under various real-world conditions.
- Evaluate the performance of AI models by assessing speed, efficiency, scalability, and resource utilization.
- Perform manual and automated testing on AI-based applications, platforms, and solutions.
AI Model Accuracy and Validation
- Test AI models for accuracy, precision, recall, F1 score, and other performance metrics.
- Ensure AI models fairness by conducting tests for potential bias in decision-making processes, especially in clinical or medical applications.
- Validate AI model predictions against real-world data, ensuring that results are consistent, reliable, and actionable.
Collaboration and Knowledge Sharing
- Work with data scientists, AI engineers and Test Manager to improve testing methodologies and continuously optimize AI model testing processes.
- Provide feedback on AI models, pointing out any potential improvements in testing coverage or areas for model retraining.
- Communicate findings, bugs, and issues related to AI models to technical teams, ensuring prompt resolution.
Test Automation for AI Projects
- Develop and implement automated testing scripts and frameworks specifically designed for AI applications.
- Utilize AI testing tools and frameworks (RAGAS etc.) to automate the validation of AI models and algorithms.
- Integrate automated AI testing within continuous integration and continuous deployment (CI/CD) pipelines.
Compliance and Regulatory Testing
- Ensure that AI applications comply with industry-specific regulations, especially in the pharma and healthcare sectors (e.g., FDA regulations, HIPAA compliance).
- Verify that all AI-driven processes adhere to ethical standards and data privacy laws.
Continuous Improvement and Research
- Stay up-to-date with the latest trends, tools, and techniques in AI testing and apply these advancements to optimize the testing process.
- Participate in AI testing forums and workshops, contributing insights to improve best practices within the team.
Reporting and Documentation
- Document test results, methodologies, and issues clearly, providing insights into test coverage, risk analysis, and performance benchmarks.
- Prepare detailed reports for both technical and non-technical stakeholders, summarizing testing outcomes and potential risks associated with AI implementations.
- Assist in the creation and maintenance of knowledge-sharing platforms related to AI testing best practices.
Key Skills and Qualifications:
Technical Expertise
- Strong knowledge of AI/ML testing methodologies and best practices.
- Experience with AI development frameworks and libraries such as TensorFlow, Keras, PyTorch, scikit-learn, RAGAS and MLlib.
- Proficient in testing tools and environments for AI-based systems (e.g., Jupyter Notebooks, Apache Spark, and DataRobot ).
- Experience with performance testing tools like Grafana and JMeter for AI solutions.
- Knowledge of Python, R, JavaScript or other programming languages frequently used in AI/ML.
- Understanding of test automation frameworks and experience in tools like Cypress , and JUnit for automating AI tests.
AI Model Evaluation
- Solid understanding of machine learning and deep learning models, including supervised and unsupervised learning techniques.
- Familiarity with evaluating AI models on metrics such as accuracy, precision, recall, F1 score, confusion matrices, and AUC.
- Ability to identify and test for model biases, fairness, and ethical implications, especially in sensitive applications like healthcare and pharma.
Analytical and Problem-Solving Skills
- Strong problem-solving abilities and keen attention to detail, with a systematic approach to diagnosing and resolving AI-related issues.
- Ability to perform root cause analysis of issues in AI algorithms and suggest actionable fixes.
Collaboration and Communication
- Excellent teamwork and communication skills, with the ability to collaborate with cross-functional teams, including data scientists, engineers, and product managers.
- Strong verbal and written communication skills to convey technical information clearly and concisely to both technical and non-technical stakeholders.
Experience
- Minimum of 8 years experience in software testing, with at least 2 years focused on testing AI/ML models or AI-based applications.
- Proven experience in testing AI/ML algorithms in production or staging environments.
- Experience working in a regulated industry (such as pharmaceuticals or healthcare) is a plus.
Preferred Qualifications:
- Experience with cloud platforms (e.g., AWS, Azure) for deploying AI applications and models.
- Familiarity with DevOps practices and integrating AI testing into CI/CD pipelines.
- Certification in AI/ML or related testing frameworks (e.g., Certified Software Test Professional (CSTP) or Certified AI Engineer ).
- Knowledge of big data technologies (e.g., Hadoop, Spark) and how they integrate with AI models.
Employment Type: Full Time, Permanent
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based on 401 employees
Flexible timing
Monday to Friday
No travel
Day Shift
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