Senior QA Automation

Title Quality Engineer Location - Indianapolis, IN and Portsmouth, NH, Boston, MA (Hybrid) Duration 3 months contract Job Description
The GRS Quality Engineering (GRS-QE) organization is seeking a dynamic, hands-on Solutions Engineer to lead and deliver innovative quality-engineering solutions across a large application portfolio. Your focus will be Gen-AI driven enterprise test automation, quality insights, performance engineering, data-validation frameworks, and tight collaboration with Site Reliability Engineering to build observability into every stage of the SDLC. You ll combine deep technical expertise with strong leadership to architect, implement, and scale modern testing platforms that enable teams worldwide to ship faster and safer.
About the Job:
Lead the end-to-end delivery of quality-engineering solutions that meet customer needs while aligning with technology guardrails and strategic roadmaps.
Partner with Site Reliability Engineering (SRE) to embed observability metrics, logs, traces into automated tests and quality gates, reducing MTTR and boosting incident readiness.
Drive Gen-AI/LLM experimentation (self-healing tests, autonomous test-case generation, AI-assisted defect triage) and transition validated prototypes into production platforms.
Guide feature teams on modern quality practices: Shift-Left, DevTestOps, contract testing, performance engineering, data validation, and program-level quality governance.
Integrate and optimize vendor testing platforms/tools (e.g., PractiTest, LambdaTest, LoadRunner, UIPath) to maximize business value and ROI.
Collaborate with product managers, architects, engineers, and business stakeholders to translate requirements into scalable quality solutions, and quality solutions into effective delivery decisions.
Work with application teams to apply testing strategy, standards, and tools to agile delivery with confidence
Facilitate global knowledge sharing, cross-team alignment, and continuous improvement across geographically dispersed squads.
Participate in incident management, root-cause analysis, and post-mortems; provide consultative recommendations to prevent recurrence.
Contribute to funding prioritization and roadmap planning to ensure efficient use of resources.
Mentor and coach engineers, fostering technical growth and promoting agile best practices (Scrum, Kanban).
Utilize design thinking, DevTestOps pipelines, and infrastructure-as-code to improve speed and quality.
Stay abreast of emerging technologies in genAI, cloud architecture, containerization, microservices, and observability; apply relevant innovations to ongoing work.
Document architectures, quality standards and strategy thoroughly for maintainability and knowledge transfer.
We encourage you to apply if you are a:
Strategic Partner able to connect the dots between business outcomes, customer experience, and technical architecture.
Proficient Communicator able to effectively communicate to multiple levels of org leadership
Systems-Level Engineer comfortable whiteboarding end-to-end flows for microservices, event buses, data contracts, and test hooks knowing where the risks live.
AI-Curious Practitioner always exploring Gen-AI, ML, and autonomous testing trends, turning hype into production value.
Change Agent skilled at leading by influence, facilitating consensus across dev, ops, product, SRE, and leadership stakeholders.
Mentor & Multiplier committed to raising the bar for everyone around you through coaching, pairing, and knowledge sharing.
Qualifications
Bachelor s or master s degree in Computer Science, Engineering, or a related discipline (or equivalent experience).
10+ years of hands-on software or quality engineering experience with demonstrated technical leadership.
Proven expertise in building and integrating large-scale test automation frameworks within AWS environments.
Hands-on skills with at least two of the following: Playwright, UIPath, RestAssured, Postman, Pact.
Experience embedding automated tests into CI/CD pipelines using GitHub Actions, Azure DevOps, or similar.
Solid understanding of cloud-native architectures, microservices, containerization (Docker/Kubernetes), and infrastructure-as-code.
Familiarity with performance/load testing tools (k6, JMeter, LoadRunner) and observability platforms (DataDog).
Exposure to Gen-AI/LLM-powered testing tools (Copilot, OpenAI) or custom prompt/embedding solutions.
Strong communication, facilitation, consensus-building, and stakeholder-management skills.
Proven ability to mentor and elevate team technical capabilities.
Relevant certifications (AWS, testing tools, or observability) are a plus.