Enterprise GenAI Architect

Job Description

Job Description

Job Description/ Responsibilities:
AI-Driven Test Automation Transformation
• 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 GitHub 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.
Architecture & Solution Design
• 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.
Strategy & Roadmap
• 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.
Collaboration & Leadership
• 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.
Innovation & Experimentation
• 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 LangChain, LangGraph, 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.

What are the top skills required for this role?
• 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, LangChain, LangGraph, GitHub 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.