Senior Software Development Test Enigneer

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 3000 people across North America Asia and Europe.

Roles & Responsibilities

Generative 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 relevancegroundedness and answer faithfulness using frameworks like RAGAS orDeepEval

• Design test beds tovalidatemulti-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 agentbehaviouracross 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

• StatisticallyvalidateAI 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

• Maintainautomated 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 andmaintainscalable test automation frameworks for APIs backend services and model endpoints

• Embed AI evaluation and data QA suites intoMLOpsand CI/CD pipelines so quality failures block releases automatically

• Define and track AI quality KPIs and communicate release readiness to engineering and product teams

Experience of 5 years SDET role

Technical Skills & Frameworks

Core Programming

• Python expert level; test automation eval pipelines data analysis (Pandas NumPyPytest)

• SQL data output validation ground truth querying pipeline data quality checks

GenAI & Evaluation

• RAGAS /TruLens/DeepEval/Promptflowetc LLM evaluation frameworks for measuring faithfulness hallucination rate and task success

• LangChain/LangSmith/LlamaIndex agent workflow testing prompt tracing and LLM response debugging

• OpenAI / Anthropic /HuggingFaceAPIs direct LLM endpoint testing and output consistency validation

• Vector DBs retrieval quality testing embedding validation and latency benchmarking

• Pandas / NumPyetc. statistical analysis for output validation and error patterninvestigation dataprofiling schema validation and pipeline integrity checks

API & Automation

• Pytest modular reusable test framework for AI eval and automation suites

• Postman/ REST Assured / Requests API contract validation and service-level integration testing

MLOps& CI/CD

• MLflow tracking model versions and eval runs to detect regressions across updates

• Docker / GitHub Actions / Jenkins containerised test environments and deployment pipeline automation

Observability

• Grafana / Kibana /OpenTelemetry monitoring AI system health output drift and distributed tracing across agent pipelines

Good to Have

• Cloud AI Services AWS Bedrock Azure OpenAI or GCP Vertex AI for testing managed model endpoints and cloud-deployed agents

• MLOpsPlatformsMLflow Kubeflow Weights & Biases or Feast feature stores for experiment tracking and model governance

• ML Frameworks Scikit-learn TensorFlow orPyTorchfamiliarity for understanding model internals and validating training pipelines

• Infrastructure as Code Docker KubernetesTerraformfor managing containerised test environments at scale

• UI Automation Playwright or Cypress for end-to-end conversational AI application testing

• Performance Engineering Locust or JMeter for load testing heavy AI inference endpoints under peak traffic

• Statistical Hypothesis Testing t-tests confidence intervals significance testing to distinguish real quality signal from noise

• Synthetic Data Generation using LLMs to generate diverse test cases and evaluation datasets at scale

Tekion is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age status as a protected veteran status as an individual with a disability victim of violence or having a family member who is a victim of violence the intersectionality of two or more protected categories or other applicable legally protected characteristics.

For more information on our privacy practices please refer to our Applicant Privacy Notice here.