Quality Assurance Engineer
Millennium Systems International is an exciting and dynamic software company based in Parsippany, New Jersey. The company has built a reputation for producing high-quality, award-winning applications for the Wellness and Spa industries since 1987. Its commitment to delivering excellent customer service has contributed to its ongoing success. We are in search of a Quality Assurance Engineer – AI-Augmented Quality Engineering to help supplement the growth and scaling of the company. Millennium Systems International is honored to have been named one of New Jersey's Top Workplaces for 2021-2025!
About the Role:
We are looking for a forward-thinking Quality Assurance Engineer who combines deep QA expertise with a passion for applying AI and intelligent tooling to transform how we test, validate, and ship software. This role goes beyond traditional testing — you will be a force multiplier for the QA organization, leveraging large language models, AI-powered test generation, intelligent automation, and data-driven insights to accelerate quality across the full software delivery lifecycle.
You'll partner closely with engineering, product, and data teams to embed AI-assisted practices into everyday QA workflows — from smarter test case design and automated defect triage to predictive risk analysis and self-healing test frameworks. This is a remote position.
What We're Looking For:
Core Quality Engineering Skills
- Strong foundation in Quality Assurance and Quality Engineering fundamentals, including end-to-end testing strategy, risk-based testing, and release readiness.
- Proven experience designing and executing tests across web and API layers — including UI validation, regression, integration, and exploratory testing.
- Hands-on proficiency with test automation frameworks such as Playwright, Selenium, or Cypress, with the ability to architect scalable, maintainable test suites.
- Comfortable working with SQL for test data setup, validation, and troubleshooting data integrity issues across environments.
- Solid understanding of CI/CD pipelines and the ability to integrate automated checks into build and deployment workflows.
- Experience working within Agile teams, contributing to sprint planning, test estimation, and release coordination.
- Demonstrated ability to leverage AI-powered tools (e.g., GitHub Copilot, Claude, ChatGPT, Cursor, or similar LLM-based assistants) to accelerate test case generation, script authoring, defect analysis, and documentation.
- Experience with — or strong aptitude for — prompt engineering techniques to extract high-quality, context-aware outputs from AI models for QA workflows.
- Familiarity with AI-assisted test automation approaches such as self-healing locators, visual AI testing (e.g., Applitools), or intelligent test selection/prioritization.
- Ability to evaluate, pilot, and integrate new AI-powered QA tools into existing processes, measuring impact on speed, coverage, and defect detection.
- Understanding of how to use AI for test data generation, synthetic data creation, and edge-case discovery.
- Awareness of the limitations and risks of AI-generated outputs — able to validate, review, and refine AI suggestions rather than accepting them uncritically.
- A track record of mentoring junior QA engineers and elevating team capabilities through knowledge sharing, pairing, and process improvement.
- Ability to champion and evangelize AI-augmented QA practices across the engineering organization.
- Experience identifying quality gaps, recommending tooling, and establishing repeatable best practices that scale.
- Strong communication and documentation skills — able to articulate technical concepts clearly for both engineering and non-technical stakeholders.
- Proactive, self-directed, and dependable, with a history of independently driving initiatives from concept through execution.
- Define and execute AI-augmented test strategies across functional, integration, and automation testing layers.
- Leverage AI tools to accelerate QA workflows — including test case design, automation script generation, defect root-cause analysis, and test documentation.
- Architect and maintain scalable test automation frameworks, integrating AI-assisted capabilities such as intelligent test selection, self-healing tests, and predictive defect analysis.
- Evaluate and pilot emerging AI-powered QA tools, building proof-of-concepts and making recommendations for adoption based on measurable quality and efficiency gains.
- Mentor and coach QA team members on both foundational QE practices and the effective use of AI tools in their daily work.
- Collaborate with engineering and product teams to embed quality earlier in the development lifecycle, advocating for shift-left testing and AI-supported code review.
- Contribute to CI/CD pipeline quality gates, ensuring automated functional checks are reliable, fast, and actionable.
- Establish and share best practices for responsible, effective use of AI in quality engineering across the organization.
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field.
- 5–8+ years of professional experience in software Quality Assurance / Quality Engineering.
- Strong programming skills in one or more languages: Python, JavaScript, TypeScript, C#, or equivalent.
- Proven experience building and maintaining test automation at scale using modern frameworks (Playwright, Selenium, Cypress, or similar).
- Demonstrated, hands-on experience using AI/LLM-powered tools to improve QA efficiency, coverage, or speed.
- Experience with API testing and CI/CD integration.
- Strong analytical and problem-solving skills with excellent attention to detail.
- Excellent written and verbal communication skills.
- Experience with ETL / Data Warehouse testing across tools such as Fivetran, Databricks, Snowflake, or similar.
- Familiarity with data modeling concepts (star schema, fact/dimension tables) and data formats (Parquet, CSV, JSON).
- Experience validating end-to-end data flow from source systems through ETL processes to curated layers and visualization tools (Quicksight, Tableau, Power BI).
- Experience with data reconciliation — row counts, aggregations, and KPI accuracy.
- Familiarity with non-functional testing practices — performance, scalability, failover, reliability, and security.
- Hands-on experience with performance testing tools (JMeter, Locust, k6) or visual AI testing tools (Applitools).
- Experience with backend/API testing using tools such as Postman, REST Assured, or equivalent.
- Experience building custom AI agents, workflows, or integrations using APIs from LLM providers.
- Familiarity with observability and monitoring tools (Datadog, New Relic, Grafana).
- Knowledge of security testing practices and basic OWASP principles.
- Experience with test management tools such as Azure DevOps, Jira, TestRail, or Zephyr.