Mid-Level AI Software Test Engineer

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Mid-Level AI Software Test Engineer

• Location: Ann Arbor, Michigan 48104
• Work model: On-site
• Posted July 6, 2026
• Job ID: JPC - 205965

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Job Description

Mid-Level AI Software Test Engineer

Position Summary

We are seeking a Mid-Level AI Software Test Engineer to lead the quality assurance, validation, and automation efforts for AI-powered applications, machine learning systems, AI agents, copilots, and generative AI solutions. This role combines traditional software quality engineering practices with emerging AI testing methodologies to ensure AI systems are accurate, reliable, secure, scalable, and production-ready.

The ideal candidate has a strong foundation in software testing and automation, along with experience or exposure to AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, Model Context Protocol (MCP) integrations, and machine learning platforms. This role plays a critical part in establishing AI quality standards, evaluation frameworks, governance controls, and automated testing capabilities across the AI development lifecycle.

Professional Experience:

• 3-6 years of software quality engineering, QA automation, or software testing experience.
• 1-3 years of experience or practical exposure to AI, machine learning, or generative AI technologies preferred

Work Style:

This role is expected to operate with moderate independence, owning AI testing initiatives from planning through execution while collaborating closely with engineers, architects, product teams, and stakeholders to deliver high-quality AI solutions.

Mid-Level AI Software Test Engineer

(Also known as: AI Quality Engineer, Generative AI Test Engineer, AI Automation Engineer, AI Validation Engineer, SDET-AI)

Key Responsibilities

AI Quality Engineering

• Develop and execute comprehensive testing strategies for AI applications, platforms, and services.
• Validate AI-generated outputs for accuracy, consistency, relevance, reliability, and safety.
• Design and perform functional, integration, end-to-end, regression, and performance testing for AI-powered solutions.
• Create and maintain test cases for prompt-driven applications, AI agents, RAG systems, and workflow orchestration platforms.
• Validate AI guardrails, business rules, permissions models, compliance requirements, and governance controls.
• Conduct adversarial, negative, and edge-case testing to identify hallucinations, unsafe behavior, model drift, and failure scenarios.
• Establish quality benchmarks and acceptance criteria for AI solutions.

Test Automation & Evaluation

• Design, build, and maintain automated test frameworks for AI applications and services.
• Develop automated evaluation pipelines to assess AI responses, workflows, and model behavior.
• Integrate AI testing processes into CI/CD pipelines.
• Implement automated quality scoring, benchmarking, and regression detection capabilities.
• Create reusable test datasets, simulators, mocks, and validation frameworks to support scalable testing.

Platform & Integration Testing

• Test AI agents, copilots, APIs, workflow engines, MCP integrations, and tool-calling capabilities.
• Validate integrations with enterprise systems, external APIs, databases, and knowledge repositories.
• Verify performance, reliability, scalability, resiliency, and availability of AI workloads.
• Execute load, stress, and performance testing for AI applications and services.
• Identify, document, and troubleshoot defects across application, infrastructure, model, and integration layers.

Collaboration & Continuous Improvement

• Partner closely with software engineers, AI engineers, solution architects, product owners, and security teams.
• Participate in solution design reviews and provide quality-related recommendations early in the development lifecycle.
• Contribute to testing standards, methodologies, best practices, and AI quality frameworks.
• Support production readiness reviews, defect triage, root cause analysis, and continuous improvement initiatives.
• Promote responsible AI practices and help ensure alignment with organizational governance, privacy, risk, and compliance requirements.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.

• 3-6 years of experience in software testing, quality assurance, quality engineering, or test automation.

• Experience developing automated testing solutions using one or more of the following:

• Python

• Java

• JavaScript / TypeScript

• C#

• Experience with API testing and automation frameworks.

• Strong understanding of:

• Test automation methodologies

• Software Development Lifecycle (SDLC)

• Agile development practices

• CI/CD pipelines and DevOps principles

• Experience testing distributed systems, web applications, APIs, and enterprise platforms.

• Strong analytical, troubleshooting, and problem-solving skills.

• Excellent verbal and written communication skills.

Preferred Qualifications

• Experience testing:

• Generative AI applications

• LLM-based systems

• AI agents and autonomous workflows

• Retrieval-Augmented Generation (RAG) solutions

• MCP-based integrations

• Familiarity with leading AI platforms and models, including:

• OpenAI

• Azure OpenAI

• Anthropic Claude

• Google Gemini

• Experience developing AI evaluation, benchmarking, and validation frameworks.

• Experience testing cloud-native applications on:

• Microsoft Azure

• Amazon Web Services (AWS)

• Google Cloud Platform (GCP)

• Knowledge of:

• Responsible AI principles

• AI governance frameworks

• AI risk management and compliance practices

• Privacy and security considerations for AI systems

Required Skills : •Experience testing: oGenerative AI applications oLLM-based systems oAI agents oRAG applications oMCP-based integrations •Familiarity with: oOpenAI oClaude oGemini oAzure OpenAI •Experience building evaluation and benchmarking frameworks for AI solutions. •Experience testing cloud-native applications on Azure, AWS, or GCP. •Knowledge of responsible AI, AI governance, and AI risk management practices. AI initiatives are typically expected to align with enterprise governance, risk, privacy, and compliance requirements. Technical Skills

Basic Qualification :

Additional Skills :

Background Check : Yes

Drug Screen : No

Notes :

Selling points for candidate :

Project Verification Info :

Exclusive to Client :Yes

Face to face interview required :No

Candidate must be local :No

Candidate must be authorized to work without sponsorship :Yes

Interview times set :Yes

Type of project :

Master Job Title :

Branch Code :

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Job details

Job ID JPC - 205965

Location Ann Arbor, Michigan, United States

Work Model On-site

Job Type Referral Fee

Posted July 6, 2026

Skills •Experience testing: oGenerative AI applicationsoLLM-based systemsoAI agentsoRAG applicationsoMCP-based integrations•Familiarity with: oOpenAIoClaudeoGeminioAzure OpenAI•Experience building evaluation and benchmarking frameworks for AI so AWS or GCP•Knowledge of responsible AI AI governance and AI risk management practices AI initiatives are typically expected to align with enterprise governance Risk privacy and compliance requirements Technical Skills

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