Description :

Role Summary :

We are looking for an AI Test Engineer with strong experience in Python and PyTest to validate, test, and ensure the quality of AI/ML-powered applications.

The role involves designing robust test strategies for data-driven systems, validating model behavior, and ensuring reliability, performance, and correctness across AI workflows and APIs.

Experience : 3 to 6 years (adjustable)

Department : Quality Engineering / AI / ML

Location : On-site / Hybrid / Remote (as applicable)

Key Responsibilities :

- Design, develop, and maintain automated test frameworks using Python and PyTest.

- Create test cases for AI/ML pipelines, APIs, and data workflows.

- Automate regression, integration, and functional tests for AI-driven systems.

- Validate AI/ML model outputs for accuracy, consistency, and expected behavior.

- Test model inference, prediction logic, and edge cases.

- Identify data drift, model bias indicators, and anomalies in outputs (at a testing level).

- Test data ingestion, preprocessing, feature pipelines, and transformations.

- Validate training and inference pipelines across environments.

- Ensure correctness of datasets, schemas, and data versions.

- Perform API testing for AI services using REST endpoints.

- Validate response accuracy, latency, and error handling.

- Test integrations between AI services, backend systems, and third-party tools.

- Execute performance, load, and stress testing for AI services.

- Monitor response times, resource utilization, and stability.

- Validate model behavior under scale and concurrency.

- Integrate automated tests into CI/CD pipelines.

- Define quality gates for AI model releases.

- Support model deployment validation and rollback testing.

- Work closely with AI engineers, data scientists, backend developers, and product teams.

- Report defects with clear reproduction steps and data evidence.

- Maintain test documentation, reports, and coverage metrics.

Required Skills & Experience :

Core Technical Skills :

- Strong proficiency in Python.

- Hands-on experience with PyTest and test automation frameworks.

- Experience testing AI/ML-based applications or data-driven systems.

- Strong understanding of software testing principles and SDLC.

- Understanding of ML workflows (training, validation, inference).

- Ability to work with datasets, logs, and metrics.

- Familiarity with common ML libraries (scikit-learn, TensorFlow, PyTorch testing perspective)