SWQA Test Developer Intern

The incumbent will be working on helping us to build tools/utility/framework in Python/C++/C which would help automate and optimize the testing workflows in Embedded domain.

The candidate will be mainly working on Metropolis and DeepStream for inference workflows and associated microservices/apps by using AI tools to test and improve productivity and reduce the cycle time of workflows. The new intern will be guided by a Sr. Engineer and the output produced can also be used for their credentials towards the internship work done, back in college (after getting the necessary NDA/Approvals from the organization).

What youll be doing:

• Understand and design a test framework to validate metropolis and simulation uses cases of intelligent video analytics applications, define apps/workflows configuration and perform deployment of complete stack, and automate the process using Nvidias Test framework.
• Must use AI Tools to optimize test development automation.
• Define test procedures based on functional and test design specifications of IVA or inference Applications.
• Write python-based scripts, write unit tests, execute cloud scenarios, provide detailed technical analysis, prioritize the issue, and drive the action, publish weekly status report.
• The candidate should be confident to give good technical presentations and work for continuous test framework improvement.
• Deploy the test framework in Kubernetes / Helm environments.

What we need to see:

• Pursuing a degree course in computer science / electronics / telecommunication with strong academics.
• Good Communication skills (interpersonal and across teams).
• Excellent analytical and problem-solving skills.
• Very organized, proactive, and has good learning ability.
• Languages - Python/C/C++.
• Strong in OOPs concepts and Data structures.
• Having development experience using AI development tools for testing.
• Knowledge of SQL/NoSQL Database management tools.
• Familiarity of cloud architecture viz. AWS, Azure, etc. and Helm/K8 deployments.
• Familiarity of Linux, AI/ML/DL, message broker like Kafka Tools, Spark, Elasticsearch, Logstash, and Kibana.

Ways to stand out from the crowd:

• AI tool usage and testing optimization using the same.
• Any certification course on Machine learning.
• Knowledge of deployment technologies like Kubernetes.