IT Software Engineer 4

Job Title: IT Software Engineer 4 AI Architect for Testing - Generative AI Architect Quality Engineering Transformation

Location: Remote (Anywhere in the U.S.)

Client: Doosan

Pay: $65hr W2

Duration: 12 Months

Position Overview

Seeking an experienced AI Architect for Testing to lead enterprise-wide transformation initiatives focused on applying Artificial Intelligence and Generative AI within Quality Engineering and Software Testing organizations.

This role will drive the strategy, architecture, and implementation of AI-enabled testing solutions designed to improve software quality, accelerate testing efficiency, enhance automation capabilities, and modernize QA operations across the SDLC/STLC lifecycle.

The ideal candidate will possess a strong background in QA automation, enterprise testing architecture, AI/ML technologies, and modern software engineering practices. This individual will partner closely with QA teams, developers, DevOps engineers, and product stakeholders to integrate AI-driven capabilities into enterprise testing ecosystems.

Work Location

• 100% Remote within the United States

• Candidates located near Chicago may be expected to follow a hybrid schedule:

• Approximately 2 3 days onsite per week

• Or as needed based on business requirements

Key Responsibilities

AI Strategy & Testing Transformation

• Define and lead the AI vision and strategy for enterprise testing organizations
• Establish AI adoption roadmaps and best practices for QA teams
• Drive AI-enabled transformation initiatives across testing and quality engineering functions
• Identify opportunities where AI can improve testing efficiency, coverage, and quality

AI-Driven Testing Solutions

Design And Implement AI-powered Solutions For

• Test case generation
• Test data creation
• Automated test maintenance
• Defect prediction
• Root cause analysis
• Failure and log analysis
• Intelligent regression testing
• Risk-based test prioritization
• AI-assisted automation workflows

Framework & Architecture Design

Design Scalable AI-enabled Testing Frameworks Supporting

• Functional testing
• API testing
• UI automation and validation
• Performance testing
• Security testing support

QA Engineering & DevOps Integration

• Integrate AI capabilities into CI/CD pipelines and quality engineering workflows
• Collaborate with QA, Engineering, Product, and DevOps teams
• Support enterprise-scale testing modernization initiatives
• Promote AI-assisted development and testing methodologies

Leadership & Enablement

• Mentor QA engineers and automation teams on AI-assisted testing practices
• Lead proofs of concept, pilot programs, and enterprise rollouts
• Establish standards and governance for AI adoption within QA organizations

Required Qualifications

Education

Bachelor's Or Master's Degree Preferred In

• Computer Science
• Engineering
• Data Science
• Related technical disciplines

Experience

• 10 years of overall industry experience

• Strong background in:

• Software Testing

• QA Automation

• Quality Engineering

• Test Architecture

• Minimum 3+ years of experience implementing:

• Artificial Intelligence solutions

• Machine Learning systems

• Generative AI solutions

• Enterprise AI initiatives

Note: Machine learning experience may contribute toward the AI requirement; however, candidates must demonstrate direct exposure to AI/GenAI technologies.

Required Technical Skills

AI / Machine Learning

Hands-on Experience With

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Machine Learning
• Prompt Engineering
• Enterprise AI implementations
• AI-assisted workflows and automation

Programming Languages

Highest Priority

• Java
• SQL

Additional Preferred Languages

• Python
• JavaScript

Testing Tools & Frameworks

Top Required Tools

• Selenium
• Playwright

Additional Preferred Tools

• Karate
• TestNG
• API testing frameworks/tools
• Tosca (preferred)

Quality Engineering & SDLC Knowledge

Strong Understanding Of

• SDLC (Software Development Life Cycle)
• STLC (Software Testing Life Cycle)
• QA operations and testing methodologies
• Automation frameworks
• CI/CD pipelines
• Quality engineering best practices

Preferred Qualifications

• Experience building AI copilots or AI-assisted QA solutions

• Experience integrating AI into enterprise testing environments

• Familiarity with:

• Retrieval-Augmented Generation (RAG)

• AI agents

• Workflow automation

• Observability and log analysis tools

• Scalable enterprise architecture design

• Experience working in Agile/Lean environments

• Knowledge of DevOps and modern engineering practices

• Exposure to cloud platforms such as AWS, Azure, or GCP

Interview Process

• Multiple interview rounds
• Primarily remote / Microsoft Teams-based interviews
• In-person interviews are no longer mandatory

Assessment Focus

The Interview Process Will Emphasize

• AI-assisted problem solving
• AI-enabled development workflows
• Demonstration of AI tool usage
• Practical application of AI within testing environments

Traditional SDET-style coding assessments will have reduced emphasis.

Top Skills Identified By Hiring Team

• Enterprise AI / Generative AI experience
• Knowledge of LLMs, NLP, Machine Learning, and Prompt Engineering
• Strong QA automation and testing process expertise
• Deep understanding of SDLC/STLC and Quality Engineering practices
• Hands-on experience with Selenium, Playwright, Java, and SQL

Candidate Disqualifiers / Red Flags

• Frequent job changes or unstable tenure history
• Limited or no direct AI/Generative AI exposure

Weak understanding of QA/testing operations and automation processes