QA Technology Delivery Lead – Healthcare

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.