AI Test Engineer

Intone Networks
McLean, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

McLean, United States of America

Tech stack

API
Artificial Intelligence
Cluster Analysis
Github
JSON
Metadata
Markdown
Test Case Design
Test Execution Engine
Large Language Models
Test Scripts
Gherkin
Playwright
Data Management
Microservices

Job description

  • Design And Implement Agentic Testing Patterns That Can Be Adopted By Multiple Underwriting Teams (And Later Other Domains).
  • Create Reference Implementations (Sample Repos / Templates) Demonstrating:
  • Test Generation Assistance (From Requirements, Apis, Contracts, Schemas)
  • Test Maintenance Assistance (Auto-Updating Selectors/Contracts, Flaky Test Triage)
  • Failure Analysis Assistance (Root Cause Suggestions, Log Correlation, Defect Drafting)
  • Establish A Standard Architecture For Test Code Organization, Tagging, Data Management, And Execution Across Ui + Api + Service Layers.
  1. Coverage Standards, Templates, And Governance
  • Define And Publish Coverage Standards (What "Good" Looks Like) Including:
  • Minimum Coverage Expectations By Service/Component
  • Test Type Mix (Unit Vs Api Vs Ui Vs Contract Vs Integration)
  • Risk-Based Prioritization And Traceability To Requirements
  • Provide Templates Usable Across Teams:
  • Test Plan Templates
  • Test Case/Spec Templates (Gherkin-Style Or Equivalent)
  • Definition Of Ready / Definition Of Done Quality Checklists
  • Create A Scalable Tagging/Metadata Strategy (E.G., Feature, Service, Risk, Priority, Data Sensitivity) To Support Reporting And Quality Gates.
  1. Genai-Assisted Reporting And Quality Insights Across Microservices
  • Build Automated Reporting That Aggregates Test + Service Data Across Multiple Microservices, Such As:
  • Test Execution Results (Karate/Playwright + Ci Runs)
  • Service Health Signals (Logs/Metrics/Traces If Available)
  • Defect Signals (Issue Tracker Metadata If Available)
  • Generate Genai-Driven Summaries:
  • Release Readiness Narratives
  • Failure Clustering And Trend Analysis
  • "What Changed?" Insights (Commit/Pr Correlation)
  • Produce Outputs Consumable By Engineering Leadership And Teams (Dashboards, Markdown Summaries In Prs, Artifacts In Ci).
  1. "Quality Gates" Via Agents
  • Build Automated Review Agents That Evaluate User Stories/Requirements For Minimum Required Clarity And Data Before Development/Testing Starts:
  • Required Fields Present (Acceptance Criteria, Testable Outcomes, Data Needs, Dependencies)
  • Ambiguity Detection And Missing Edge Cases
  • Data/Privacy Considerations And Environment Needs
  • Integrate Gates Into Workflow (Pr Checks, Issue Templates, Github Actions) To Reduce Churn And Rework.

Requirements

  • Must Have 5+ Years Of Experience And A Strong Ai Development Background.
  • Must Have Hands-On Experience Building Agentic Workflows, Automating Testing Using Ai, And Working With Tools Such As Github, Copilot, And Claude., * Hands-On Experience Building Llm-Powered Agents (Tool-Using, Multi-Step Reasoning, Guardrails).
  • Experience With Prompting Patterns, Structured Outputs (Json Schemas), Evaluation, And Reducing Hallucinations.
  • Ability To Design Agent Workflows For:
  • Test Generation/Augmentation
  • Requirements Review And Completeness Validation
  • Report Generation And Summarization

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