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 & 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 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
Embed AI evaluation and data QA suites into MLOps and 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 8+ years SDET role
Technical Skills & Frameworks
Core Programming
Python — expert level; test automation, eval pipelines, data analysis (Pandas, NumPy, Pytest)
SQL — data output validation, ground truth querying, pipeline data quality checks
GenAI & Evaluation
RAGAS / TruLens / DeepEval / Promptflow etc — 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 / Hugging Face APIs — direct LLM endpoint testing and output consistency validation
Vector DBs — retrieval quality testing, embedding validation, and latency benchmarking
Pandas / NumPy etc. — statistical analysis for output validation and error pattern investigation, data profiling, 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
MLOps Platforms — MLflow, Kubeflow, Weights & Biases, or Feast feature stores for experiment tracking and model governance
ML Frameworks — Scikit-learn, TensorFlow, or PyTorch familiarity for understanding model internals and validating training pipelines
Infrastructure as Code — Docker, Kubernetes, Terraform for 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.
