Website:
arktecq-global.com
Job details:
Company Description
Arktecq is a trusted global business partner for technology providers and IT companies. Headquartered in New Jersey, the company operates across the United States, Canada, and India. With a commitment to delivering innovative solutions, Arktecq helps businesses achieve their objectives through advanced technologies and efficient processes. The company prides itself on fostering a culture of growth, collaboration, and innovation.
Role Description
We are seeking a detail-oriented and innovative QA AI Engineer to ensure the quality, reliability, and performance of AI/ML-based systems. The ideal candidate will be responsible for designing and executing testing strategies for AI models, validating data pipelines, and building robust QA frameworks tailored for AI applications. This role also involves contributing to the development of internal AI testing frameworks and automation tools.
1. AI/ML Testing & Validation
- Design, develop, and execute test plans for AI/ML models and data-driven systems.
- Validate model accuracy, performance, robustness, and fairness.
- Perform functional, regression, integration, and system testing for AI-powered applications.
- Conduct data validation and quality checks on training and inference datasets.
- Identify model drift, bias, and performance degradation issues.
2. Framework Development
- Design and build scalable QA frameworks for testing AI/ML systems.
- Develop automation tools for model validation, API testing, and pipeline verification.
- Create reusable testing libraries and utilities for AI validation.
- Integrate QA frameworks into CI/CD pipelines for continuous testing.
- Establish metrics and benchmarks for AI model evaluation.
3. Automation & Performance Testing
- Automate model testing workflows using Python and relevant testing libraries.
- Perform load, stress, and performance testing for AI APIs and services.
- Validate real-time and batch processing AI systems.
4. Collaboration & Documentation
- Collaborate with Data Scientists, ML Engineers, and DevOps teams to ensure quality at every stage.
- Document test strategies, processes, and framework architecture.
- Provide feedback on model design from a testability and reliability perspective.
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