AI Architect (for Testing)

Diversified Services Network, Inc.•Chicago, IL

6d•$155,000 - $165,000•Hybrid

About The Position

Diversified Services Network, Inc. (DSN) is seeking a full-time AI Architect (for Testing) to join our team in Chicago, IL! We offer full benefits, PTO, 401k, and more! If you’re looking to shape the future of AI-driven quality engineering within an extremely reputable, stable Fortune 500 company — let’s talk! Position Overview We are seeking a visionary AI Architect to lead how our testing organization leverages artificial intelligence to improve quality, speed, coverage, and insight across the software delivery lifecycle. You will define the AI strategy for QA and testing, design AI-enabled testing solutions, guide tool selection and integration, and help teams adopt AI responsibly in day-to-day testing activities. You will work within a team of 20+ professionals, partnering closely with QA engineers, developers, DevOps teams, product managers, and data teams to embed AI into CI/CD and quality engineering workflows at scale.

Requirements

8–10 years of overall experience required.
Bachelor’s degree in Computer Science, Engineering, or a related technical field required.
8+ years in software testing, QA automation, quality engineering, or test architecture.
3+ years designing or implementing AI/ML or GenAI-based solutions in enterprise environments.
Strong understanding of software testing methodologies, test automation frameworks, SDLC/STLC, CI/CD pipelines, and quality engineering practices.
Experience with AI technologies including machine learning models, NLP, LLMs, prompt engineering, retrieval-augmented generation (RAG), and AI agent/workflow automation.
Hands-on experience with Python, Java, JavaScript, and/or SQL; proficiency with test tools such as Selenium, Playwright, Cypress, Appium, JUnit, TestNG, or PyTest.
Experience integrating with platforms such as Jira, Azure DevOps, GitHub, Jenkins, or GitLab.
Knowledge of test analytics, data pipelines, and observability/log analysis tools.
Strong architecture and design skills for scalable, secure enterprise solutions.
At least 2 years of experience working on an Agile team with a solid understanding of Agile/Lean practices.
Ability to adapt quickly in complex, fast-moving environments; proactive, flexible, and creative in approach.
Very strong communication and collaboration skills — able to work effectively with both technical developers and non-technical business stakeholders.
Strong stakeholder communication and governance mindset, with the ability to influence AI adoption responsibly across the organization.

Nice To Haves

Experience building AI copilots or assistants for QA teams.
Familiarity with vector databases, model evaluation, and MLOps/LLMOps concepts.
Experience with cloud platforms (AWS, Azure, or GCP); AWS Cloud development experience is a strong plus.
Experience with TDD, continuous integration, and code review practices.
Experience with Apigee or other API gateways; familiarity with microservice architecture.
Experience with load and performance testing; understanding of regulated environments and compliance requirements.
Certifications in test architecture, cloud, AI/ML, or enterprise architecture.
Strong documentation skills and experience with technical writing for architecture and governance standards.

Responsibilities

Define the AI vision and architecture for the testing function; identify opportunities to improve testing activities through AI, including test case generation, test data creation, automated test maintenance, defect prediction, root cause analysis, log and failure analysis, requirements-to-test traceability, risk-based test prioritization, and intelligent regression selection.
Design and implement AI-enabled frameworks for functional testing, API testing, UI automation, performance testing, security testing support, and exploratory testing augmentation.
Create architecture for integrating LLMs, test repositories, defect systems, observability tools, and CI platforms into a cohesive AI-powered testing ecosystem.
Evaluate and recommend AI/ML, GenAI, and test automation tools; establish standards for AI governance, privacy, security, explainability, and responsible usage in testing.
Define metrics to measure AI effectiveness in testing — including defect leakage, automation coverage, test execution efficiency, flaky test reduction, mean time to diagnose failures, and release quality trends.
Mentor QA engineers and SDETs on AI-assisted testing practices; lead pilots, proofs of concept, and scaled rollouts of AI testing capabilities.
Ensure compliance with enterprise policies for data handling, model usage, and responsible AI deployment.