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.

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