Sdet

DocuSign DocuSign · Enterprise · Bangalore, India · IT Infrastructure & Operations

Software Development Engineer in Test (SDET) responsible for designing, developing, and maintaining scalable test automation frameworks for enterprise SaaS platforms. A critical differentiator is the ability to independently apply AI testing methodologies, including LLM evaluation, prompt testing, hallucination detection, and AI-assisted STLC practices, to ensure quality for both traditional and AI-powered product features. This role will leverage internal AI quality tooling and evaluator frameworks to accelerate test delivery and improve coverage consistency. The role also involves testing training/validation datasets and model quality testing.

What you'd actually do

  1. Apply Docusign's AI testing framework to evaluate LLM-powered features and agentic AI workflows (e.g., Quality Agent, XDR Agents, Leads Agent)
  2. Design and execute prompt test suites to validate LLM output accuracy, consistency, tone, and alignment with expected outcomes across diverse input scenarios
  3. Conduct hallucination detection and post-processing fact-checking for LLM-generated content; validate that AI outputs are grounded in retrieved context
  4. Validate training/validation datasets for missing values, data leakage, class imbalance, duplicates, and biased sampling
  5. Execute model quality testing using metrics such as accuracy, precision/recall, F1, ROC-AUC, and regression error (MAE/RMSE) as applicable to the model type

Skills

Required

  • design, develop, and maintain scalable test automation frameworks
  • AI testing methodologies
  • LLM evaluation
  • prompt testing
  • hallucination detection
  • AI-assisted STLC
  • evaluator frameworks (e.g., Arize AX)
  • agentic AI workflows
  • prompt test suites
  • model quality testing
  • training/validation datasets
  • RAG-based AI features
  • performance and load testing strategies
  • CI/CD integration
  • data validation automation

Nice to have

  • Salesforce
  • Oracle ERP
  • MuleSoft integrations
  • UiPath workflow automation
  • CrewAI
  • Azure OpenAI
  • Azure AI Search
  • JMeter
  • k6
  • GitLab CI

What the JD emphasized

  • independently apply AI testing methodologies
  • LLM evaluation
  • prompt testing
  • hallucination detection
  • AI-assisted STLC
  • agentic AI workflows
  • prompt test suites
  • hallucination detection
  • training/validation datasets
  • model quality testing

Other signals

  • AI testing methodologies
  • LLM evaluation
  • prompt testing
  • hallucination detection
  • AI-assisted STLC
  • Quality Agent powered by CrewAI
  • evaluator frameworks (Arize AX)
  • agentic AI workflows
  • RAG-based AI features