Staff Software DevelopmentTest Engineer (AISDET)

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.

• Develop a deep understanding of Tekion’s AI Platform architecture, services, data/feature flows, and the ML lifecycle it supports.
• Own end-to-end quality for platform capabilities — feature pipelines, training and serving infrastructure, model registry, deployment pipelines, LLM gateways, and vector/data stores.
• Design and implement automated testing frameworks for platform APIs, SDKs, services, and infrastructure components.
• Validate the correctness, completeness, and consistency of features and model inputs/outputs as they move through the platform.
• Test model-serving and inference services for correctness, latency, throughput, scalability, and reliability under load.
• Build regression and reconciliation suites for large-scale platform changes, migrations, and 
  version upgrades.
• Validate platform capabilities such as ingestion, feature computation, model deployment/rollback, multi-tenancy, and governance.
• Define quality metrics and SLO validation, with automated monitoring to detect platform anomalies before they impact ML teams or customers.
• Contribute to evaluation frameworks for platform-level AI services (e.g., serving correctness, feature integrity, eval-infrastructure reliability).
• Use AI/LLMs to accelerate quality engineering — generating tests, synthetic data, and automation scaffolding.
• Drive root cause analysis for production platform issues and partner with engineering teams on preventive improvements.
• Champion quality engineering and reliability best practices across the AI Platform organization.

• 8+ years in SDET, quality engineering, or software engineering, with strong experience building automation frameworks for platform, infrastructure, or backend services.
• Strong programming skills in Python and/or Java/Go, with the ability to write productionquality automation and tooling.
• Experience testing distributed systems, APIs, data pipelines, or ML/data platforms.
• Solid understanding of ML lifecycle components — feature stores, training/serving, model registry, deployment — or a strong platform-testing background with ML fluency.
• Experience with performance, load, and scalability testing.
• Familiarity with CI/CD, infrastructure-as-code, containers (Docker, Kubernetes), and cloud (AWS/GCP/Azure).
• Understanding of LLM/ML failure modes and evaluation concepts.
• Strong debugging and root cause analysis skills across service, data, and infrastructure layers.
• Excellent collaboration and communication skills.

• Experience with ML platform tools (MLflow, Feast, KServe/Seldon, Ray, Kubeflow, SageMaker/Vertex AI) or LLM infra (vLLM, model gateways, vector DBs such as Milvus/Pinecone).
• Experience with observability and monitoring stacks (Prometheus, Grafana, Datadog).
• Experience testing multi-tenant, high-throughput SaaS platforms.
• Contributions to internal test frameworks or developer tooling.
• Exposure to chaos or reliability engineering