Staff Software Development Test Engineer

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.

Roles & ResponsibilitiesGenerative AI & LLM Evaluation

• Build automated testing suites to detect hallucinations, bias, toxicity, and prompt injection vulnerabilities across LLM-powered products

• Implement automated evaluations for RAG systems measuring context relevance, groundedness, and answer faithfulness using frameworks like RAGAS or DeepEval

• Design test beds to validate multi-agent workflows — tool-calling accuracy, multi-step reasoning, memory, and autonomous decision loops

• Build and run automated conversation simulations — scripted and synthetic user journeys — to stress-test agent behaviour across intents, edge cases, and multi-turn dialog flows

• Create prompt regression frameworks to assess how changes in system prompts, temperature, and sampling parameters impact output consistency

Data Quality Assurance

• Statistically validate AI data outputs — distributions, precision/recall, error pattern analysis — to catch silent data quality failures before production

• Programmatically audit data ingestion, transformation, and feature store pipelines for schema drift and data corruption

• Validate vector DB indexing, embedding semantic similarity accuracy, and retrieval latency

• Verify quality, diversity, and privacy compliance of synthetic datasets used for model training and evaluation

Classical ML & Deep Learning Validation

• Maintain automated suites tracking ML metrics — Precision, Recall, F1, ROC-AUC — and deep learning loss curves across model versions

• Implement continuous monitoring scripts to detect data and concept drift on live inference endpoints

Automation Engineering & CI/CD

• Build and maintain scalable test automation frameworks for APIs, backend services, and model endpoints