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