AiDash - Software Development Engineer - Test III

Description :

Key Responsibilities :

- Design and implement automated test frameworks for validating ML models functional and scaling performance.

- Collaborate with ML engineers and data scientists to define robust testing strategies for model deployments.

- Develop and maintain backend automation scripts using Python / Java with frameworks such as Pytest or Rest Assured.

- Conduct performance testing, identify bottlenecks, and ensure models meet SLA and throughput requirements.

- Validate end-to-end model lifecycle, including data ingestion, training, inference, and deployment validation.

- Work closely with DevOps to integrate automated tests within CI/CD pipelines for continuous delivery.

- Ensure comprehensive test coverage across APIs, data pipelines, and orchestration layers.

- Analyze, debug, and report issues effectively, collaborating with development and platform teams for resolution.

- Create test documentation, including test plans, test cases, and defect reports.

- Contribute to and enforce testing best practices and coding standards across the QA and ML teams.

- Mentor junior SDETs and promote a quality-first engineering culture.

What Were Looking For :

Core Requirements :

- 68 years of experience in Testing and Automation, with strong backend automation experience.

- Solid understanding of testing fundamentals and best practices.

- Proficiency in Python or Java; must be comfortable coding and automating tests.

- Experience with backend test automation frameworks such as Rest Assured, Pytest, or similar tools.

- Exposure to ML application testing (validation of models, data pipelines, or inference systems).

- Strong understanding of system design, distributed backend architectures, and databases (SQL/NoSQL).

- Hands-on experience in performance testing of backend or ML systems (mandatory).

- Knowledge of application deployment and orchestration concepts (e.g., Docker, Kubernetes).

- Understanding of CI/CD pipelines and integration processes (good to have).

- Experience using prompt-based IDEs or editors like Cursor (good to have).

Preferred Skills :

- Familiarity with ML model lifecycle testing, including validation of inference accuracy, latency, and scalability.

- Experience working with cloud environments (AWS/GCP/Azure).

- Exposure to observability and monitoring tools such as Grafana, Prometheus, or Datadog.

- Understanding of data integrity testing and API contract validation.

- Strong debugging, problem-solving, and analytical skills