AI Support Engineer

Position Summary

The QA/Support Engineer works as a member of the Operations team within Epiq s Legal Solutions division. In this role, the QA/Support Engineer plays a crucial role at Epiq by bridging quality assurance and customer/technical support, ensuring our software is tested thoroughly, reliable in production and backed by excellent technical assistance for both internal and external users.

You will work closely with the product owners, developers and other support staff to design test cases, identify and document bugs, reproduce issues and help deliver high-quality solutions. This role is ideal for someone who enjoys problem solving, has strong attention to detail and thrives in a fast-paced collaborative environment.

Essential Job Responsibilities

• Test Design & Automation

• Design, write, and execute manual and automated test cases for both traditional and AI/ML features.
• Use

  Power Automate

  to streamline repetitive QA workflows (log collection, test execution triggers, defect triage).
• Leverage

  GitHub Copilot

  to accelerate writing automated test scripts, SQL queries, and debugging code.

• AI-Specific Validation

• Validate AI outputs for accuracy, explainability, and fairness using AI testing frameworks.
• Document test results and issues clearly, using AI-assisted summarization (e.g., Copilot) to improve speed and clarity.

• Complex Data & Relationship Testing

• Use

  Lean Graph

  to model and validate relationships within large, interconnected legal datasets.
• Test data-driven scenarios such as permissions, document linkages, and case dependencies to ensure accuracy and consistency.

• Simulation & Real-World Workflow Testing

• Employ

  Sim Studio

  (or similar simulation tools) to replicate end-to-end user journeys and stress test at scale.
• Simulate edge cases (network failures, AI misclassifications, unusual input data) to validate system robustness.

• Monitoring & Support

• Utilize monitoring, logging, and alerting systems to proactively identify anomalies in AI and non-AI modules.
• Collaborate with internal/external clients, engineers, and developers to isolate, debug, and resolve AI-related issues.
• Create documentation, troubleshooting guides, and FAQs with AI assistance (Copilot, Power Automate flows) to speed delivery.

• Continuous Improvement

• Contribute to the evolution of QA processes by embeddingAI-driven test automation and simulation practices.
• Ensure QA best practices extend toAI explainability, data quality validation, and fairness testing.

Requirements

• Bachelor s degree in computer science, engineering, or related field (or equivalent experience).
• 2+ years of experience in Quality Assurance and Automation Testing, including at least 1 year of hands-on exposure to QA methodologies (unit, integration, regression, UAT) with a focus on their application in AI/ML testing environments.
• Strong experience with bug tracking, project management, and logging/debugging tools.
• Proficiency with databases (MS SQL, Snowflake, Oracle, MySQL) for test data design and validation.
• Ability to work in fast-paced environments, managing multiple priorities with high attention to detail.
• Excellent communication skills, with the ability to explain technical/AI concepts to non-technical audiences.

Tools & Technologies

Mandatory

• Automation & Productivity:

  Power Automate, GitHub Copilot, Selenium, Robot Framework

• Databases:

  MS SQL, Snowflake, Oracle, MySQL

• Monitoring & Logging:

  Splunk, ELK Stack (Elastic, Logstash, Kibana)

• Programming/Scripting:

  C#, Python, .NET basics

• Cloud Environments:

  Azure or AWS (for test environments and AI integrations)

Good-to-Have

• AI/ML Testing & Validation:

  Great Expectations, Evidently AI, Fairlearn, AIF360, MLflow

• Data & Graph Testing:

  Lean Graph (Neo4j), Pandera, Deequ

• Simulation & Workflow Testing:

  Sim Studio, JMeter, LoadRunner

• Explainability & Debugging:

  SHAP, LIME, Captum, Alibi

• Advanced Monitoring/AI Ops:

  WhyLabs, Arize AI