QA Technology Delivery Lead

Perficient is a global AI-first consultancy. Our team of strategists, designers, technologists, and engineers partners with the world’s most innovative enterprises and admired brands to deliver real business results through the transformative power of AI. As part of our AI-First strategy, we empower every employee to build AI fluency and actively engage with AI tools to drive innovation and efficiency. We break boundaries, obsess over outcomes, and shape the future for our clients. Join a company where bold ideas and brilliant minds converge to redefine what’s possible - while building a career filled with growth, balance, and purpose.           

The QA Technology Professional is a strategic, client-facing leader responsible for driving innovation in QA practices and technologies across key accounts. This role combines deep QA domain expertise with a consulting mindset to shape quality strategies, influence senior stakeholders, and support account growth.

This role partners with delivery, sales, and account leadership to understand client business objectives, design forward-thinking QA solutions, and position Perficient as a trusted advisor and integral member of the client’s leadership team. 

In addition, this role oversees ongoing QA initiatives to identify gaps, optimize processes, and define meaningful career paths for QA professionals within the organization.

This position requires close collaboration with Subject Matter Experts (SMEs) in Test Automation, Mobile Testing, ETL Testing, Accessibility Testing, and Performance Engineering to identify priorities and design comprehensive QA strategies that foster organizational growth, cross-program leverage, and technical excellence.

Disclaimer:  The above statements are not intended to be a complete statement of job content, rather to act as a guide to the essential functions performed by the employee assigned to this classification.  Management retains the discretion to add or change the duties of the position at any time. 

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QA Technical Leader 

• Write, review, and optimize automation code hands-on in Java (preferred) and/or C#/Python.
• Apply solid Object-Oriented Programming (OOP) and Design Pattern principles to keep automation frameworks maintainable and scalable.
• Balance hands-on technical contribution (coding, framework building, utility creation, debugging) with team leadership and delivery accountability.
• Own and evolve the team's automation frameworks using MCP for UI, API, Integration, and End-to-End testing using tools such as Selenium (Java-based preferred),  Playwright, Appium , WebdriverIO,  REST Assured, TestNG/Junit, Maven, etc 
• Integrate automated quality gates into CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps).
• Troubleshoot flaky tests, framework bottlenecks, and environment issues, driving root-cause fixes rather than workarounds.
• Lead, mentor, and grow a team of QA Engineers, Automation Engineers, and AI Testing specialists.
• Set technical direction for the team's automation frameworks, testing standards, and AI testing practices.
• Conduct hands-on code reviews, pair programming, and technical coaching to raise the team's engineering bar.
• Own sprint-level and release-level quality planning, test strategy, and risk-based test prioritization.
• Act as the primary technical escalation point for quality issues, blockers, and release risk decisions.
• Represent QA in architecture discussions, sprint planning, and release readiness reviews.

AI Testing & AI Agent Enablement

• Guide the team in testing AI Agents, LLM-powered features, and AI/ML models, including prompt/response validation, hallucination checks, and agentic workflow testing.
• Identify practical use cases where AI Test Agents or AI-assisted testing tools can improve coverage, speed, or maintenance effort.
• Introduce and evaluate AI-powered testing tools (test generation, self-healing automation, intelligent defect triage) and drive adoption where they add clear value.
• Define quality gates and acceptance criteria for AI/LLM-based features in partnership with Data Science and ML Engineering.
• Stay current on AI testing practices and bring practical, vetted approaches back to the team rather than chasing every new tool.

Cloud & Platform Testing

• Guide test strategy for applications deployed on AWS, Azure, and/or Google Cloud Platform (GCP).
• Support testing of containerized and Kubernetes-based services, including integration and reliability testing.
• Partner with DevOps and SRE teams on observability, monitoring, and production validation practices.
• Ensure test coverage accounts for distributed systems, microservices, and API-centric architectures.

Consulting & Client Leadership

• Act as a strategic QA advisor to client executives and product owners, holding a seat at the leadership table within the account, and lead data-driven conversations that clarify business needs and quantify quality risk.
• Facilitate discovery sessions and workshops to understand client environments and pain points, translating complex QA concepts into clear, business-oriented narratives for technical and non-technical audiences alike.

Sales, Growth & Account Strategy

• Partner with account and sales leadership to shape QA strategy within the account, identifying cross-sell/upsell opportunities and supporting pre-sales solutioning, estimation, and RFP/RFI responses.
• Build and deliver persuasive executive presentations on QA value and ROI, helping drive a long-term QA growth strategy aligned with delivery excellence and relationship expansion.

Quality Governance & Reporting

• Track and report quality metrics, defect trends, and release readiness to engineering and product leadership.
• Drive root-cause analysis and continuous improvement following production incidents.
• Ensure consistent testing standards, documentation, and best practices across the team.
• Provide clear, data-backed recommendations on go/no-go release decisions.