QA Manager

W2 | Onsite Role | Exp Level- 14+Years

Position Title: QA Manager/AI Architect (Test Automation Exp)

Location:
Charlotte, NC

Duration: 6 - 12+ Months Contract

Position Type- W2 Only

Exp Level- 14+Years

Req Skills- QA Manager, Quality Engineering (QE)/Test Architecture/Test Automation, Generative AI, Agentic AI, Prompt Engineering, Retrieval-Augmented Generation (RAG), AI-powered test automation, Python, Java, Selenium, Playwright, API Automation, LLM ecosystems and AI platforms, Azure OpenAI, AWS Bedrock, Lang Chain, Lang Graph, Git Hub Copilot, Anthropic Claude

• Lead the adoption of GenAI-powered automation across the Software Testing Lifecycle (STLC), driving productivity, quality, and speed-to-market.
• Accelerate UI, API, and end-to-end test automation through AI coding assistants and agentic development platforms such as Git Hub Copilot, Claude Code, and similar technologies.
• Design and implement intelligent agents for test case generation, test design reviews, automation script creation, defect analysis, self-healing automation, and legacy script migrations.
• Establish AI-assisted testing practices to improve test coverage, reduce manual effort, and enhance overall delivery efficiency.

• Contribute to the architecture, design, and implementation of enterprise-grade GenAI solutions and agentic frameworks.
• Develop and optimize prompt engineering strategies, retrieval workflows, and model orchestration patterns to improve solution accuracy and reliability.
• Collaborate in the design and deployment of scalable AI platforms that integrate seamlessly into SDLC and QA ecosystems.
• Participate in cross-functional GenAI initiatives, innovation programs, and Proofs of Concept (PoCs) spanning the entire software development lifecycle.

• Define and execute the GenAI adoption roadmap for Quality Engineering
  , aligned with client objectives, business priorities, and technology strategies.
• Assess build-versus-buy options and provide recommendations on AI platforms, tools, models, and vendor partnerships.
• Drive AI governance, responsible AI practices, security considerations, compliance standards, and model lifecycle management frameworks.
• Establish success metrics and value realization strategies to measure AI adoption and business impact.

• Partner with data scientists, ML engineers, architects, product owners, developers, and business stakeholders to deliver AI-powered solutions.
• Mentor engineering and QA teams on GenAI best practices, agentic workflows, prompt engineering, model optimization, deployment strategies, and AI safety principles.
• Foster a culture of innovation, continuous learning, and AI-first engineering across teams.
• Act as a thought leader and trusted advisor for GenAI adoption within the organization and client engagements.

• Continuously evaluate emerging AI technologies and identify opportunities to transform QA operations and software delivery processes.
• Develop prototypes and accelerators using modern AI frameworks such as Lang Chain, Lang Graph, Semantic Kernel, MCP, AI Skills, and multi-agent architectures
  .
• Explore advanced use cases including autonomous testing agents, conversational quality engineering assistants, intelligent release validation, and predictive quality analytics.
• Drive experimentation and innovation initiatives that improve engineering effectiveness, reduce costs, and enhance software quality outcomes.
• 16–20 years of experience in Quality Engineering (QE), Test Architecture, and Test Automation
  , with a proven track record of leading large-scale enterprise testing transformations and quality assurance programs.

• Hands-on expertise in Generative AI and Agentic AI, including Prompt Engineering, Retrieval-Augmented Generation (RAG), AI-powered test automation, and leveraging AI for intelligent test design, execution optimization, root cause analysis, and defect prediction.
• Strong technical proficiency in Python, Java, Selenium, Playwright, API Automation, and the design and implementation of scalable, reusable, and AI-enabled test automation frameworks
  .
• Deep understanding of LLM ecosystems and AI platforms, including Azure OpenAI, AWS Bedrock, Lang Chain, Lang Graph, Git Hub Copilot, Anthropic Claude
  , and related AI orchestration frameworks.
• Proven ability to define and execute Quality Engineering strategies, automation roadmaps, governance models
  , and best practices, while leading globally distributed teams and delivering measurable improvements in productivity, quality, release velocity, and cost efficiency.