Staff Software DevelopmentTest Engineer (AISDET)
Company: tekion
Location: Bangalore HQ
Type: Full-time
Posted: 2026-07-09
About this role
## About Tekion:
Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.
About the Role :
We are looking for a highly motivated Staff AI Software Development Engineer in Test (AISDET) in Test to join Tekion’s AI Platform team. Our team builds the infrastructure and tooling that powers every AI capability at Tekion — model training and serving infrastructure, feature and data pipelines, model registry and deployment, LLM gateways, vector stores, and the observability that ML Engineers and Data Scientists build on top of.
In this role, you will be responsible for building confidence in the quality, reliability, correctness, and performance of the AI Platform itself. You will work closely with Platform and Infrastructure Engineers, ML Engineers, and Product Management to define robust validation strategies, automate testing across platform services and SDKs, and ensure the platform behaves correctlyand scales under the demands of a growing fleet of AI agents.
As the platform expands to serve 100+ AI agents, you will help define quality and evaluation frameworks for platform-level AI services — validating model-...