Senior QA Engineer

Millennium Systems International•Parsippany-Troy Hills, NJ

21h•Remote

About The Position

Millennium Systems International is seeking a forward-thinking Senior Quality Assurance Engineer who combines deep QA expertise with a passion for applying AI and intelligent tooling to transform how software is tested, validated, and shipped. This role goes beyond traditional testing, aiming to be a force multiplier for the QA organization by leveraging large language models, AI-powered test generation, intelligent automation, and data-driven insights to accelerate quality across the full software delivery lifecycle. The engineer will partner closely with engineering, product, and data teams to embed AI-assisted practices into everyday QA workflows, including smarter test case design, automated defect triage, predictive risk analysis, and self-healing test frameworks. This is a remote position.

Requirements

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

Nice To Haves

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.

Responsibilities

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.