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 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 5+ 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 .