Experience: 15–18 Years

Level: Senior Manager / Associate Director

Function: Quality Engineering | Test Automation | GenAI

Employment Type: Full-Time

Location: Bangalore and Kolkata

About the RoleWe are looking for an experienced GenAI Automation Architect (SM/AD) to drive an automation-first quality engineering strategy across large-scale technology programs.

The ideal candidate will bring strong expertise in test automation architecture, cloud-native testing, CI/CD, DevOps, and modern quality engineering, combined with hands-on experience applying Generative AI and LLM technologies to accelerate and improve software testing.

You will be responsible for defining scalable automation architectures across UI, API, Mobile, and Database layers, introducing LLM-powered testing and AI-assisted automation, and establishing engineering practices that improve test coverage, quality, release velocity, and operational efficiency.

This role requires a strong technology leader who can operate at both architecture and strategic stakeholder levels, while remaining sufficiently hands-on to evaluate tools, frameworks, prototypes, and emerging GenAI capabilities.

Key ResponsibilitiesAutomation Architecture &

- Quality Engineering

- Define and drive an automation-first quality engineering strategy across multiple programs and technology platforms.
- Architect scalable, reusable and maintainable automation frameworks covering:
- UI/Web
- API/Microservices
- Mobile
- Database
- Integration and end-to-end testing
- Establish automation standards, design patterns, coding practices, framework governance and reusable components.
- Drive the transition from traditional testing approaches toward engineering-led quality and continuous testing.
- Identify automation opportunities and define the roadmap for improving automation coverage, productivity and quality.
- Evaluate and introduce modern automation tools, frameworks and accelerators.

GenAI / LLM-Powered Testing
- Design and implement GenAI-driven testing solutions using LLMs such as GPT-4/4o, Claude, Gemini, Llama and other emerging models.
- Apply Prompt Engineering techniques to generate and optimize:
- Test scenarios
- Test cases
- Test data
- Automation scripts
- Test scripts and assertions
- Defect summaries
- Root-cause analysis
- Test documentation
- Explore and implement LLM-based test generation, self-healing automation and intelligent test analysis.
- Build proof-of-concepts and production-grade GenAI accelerators for Quality Engineering.
- Establish appropriate evaluation mechanisms for GenAI solutions, including accuracy, consistency, hallucination control and output quality.
- Identify opportunities to integrate GenAI across the software testing lifecycle.

AI-Assisted Development & • Automation
- Leverage tools such as GitHub Copilot and Claude to accelerate automation engineering and developer productivity.
- Establish guidelines and governance for AI-assisted test automation and code generation.
- Drive adoption of AI coding assistants across automation teams.
- Review AI-generated automation code for quality, security, maintainability and compliance.




- Promote reusable AI-powered accelerators and engineering practices across programs.

Cloud, CI/CD & • DevOps
- Design automation solutions that integrate seamlessly with CI/CD pipelines and DevOps practices.
- Implement automated quality gates across build, deployment and release pipelines.
- Integrate automated testing into CI/CD workflows to enable continuous testing and shift-left quality.
- Design and support cloud-native testing architectures on AWS.
- Hands-on understanding of AWS services, including Amazon Bedrock, for GenAI-enabled testing solutions.
- Enable scalable execution of automation suites across cloud environments.
- Drive improvements in test execution time, pipeline reliability and release confidence.

Quality Analytics & • Engineering Intelligence
- Define and implement quality analytics and engineering metrics to measure:
- Automation coverage
- Defect leakage
- Test effectiveness
- Test execution efficiency
- Automation ROI
- Release quality
- Quality trends
- Leverage AI/analytics to identify quality risks, testing gaps and optimization opportunities.
- Develop dashboards and actionable insights for engineering and leadership teams.
- Explore platforms such as Palantir and other enterprise analytics capabilities for quality intelligence and engineering insights.

Architecture & • Technical Leadership
- Provide technical leadership and architectural direction to automation and quality engineering teams.
- Define enterprise-level automation architecture and technology roadmaps.
- Lead architecture reviews, technical assessments and solution design discussions.
- Conduct technology evaluations and recommend appropriate frameworks, tools and platforms.
- Mentor senior automation engineers, architects and technical leads.
- Establish engineering best practices across distributed teams.

Stakeholder & • Program Leadership
- Partner with Engineering, Product, Architecture, DevOps, Cloud, Data and Business teams to embed quality throughout the SDLC.
- Work closely with senior stakeholders to define quality objectives, automation roadmaps and transformation initiatives.
- Present architecture proposals, technology strategies, PoCs and transformation outcomes to senior leadership.
- Manage multiple programs and priorities while ensuring delivery against quality and automation objectives.
- Influence technology decisions and drive adoption of automation and GenAI capabilities across the organization.

Mandatory Skills
- 15–18 years of overall IT experience with significant experience in Quality Engineering / Test Automation.
- Strong experience in Test Automation Architecture and Framework Design.
- Proven experience designing automation solutions across UI, API, Mobile and Database layers.




- Strong hands-on experience with GenAI / LLM technologies.
- Practical experience with Prompt Engineering.
- Experience with one or more LLM platforms/models such as:
- GPT-4 / GPT-4o
- Claude
- Gemini
- Llama
- Strong experience with GitHub Copilot and/or Claude for AI-assisted development/testing.
- Strong understanding of CI/CD and DevOps practices.
- Experience with AWS and cloud-native testing.
- Hands-on or strong working knowledge of Amazon Bedrock.
- Experience implementing automated quality gates within CI/CD pipelines.
- Solid understanding of modern Quality Engineering, shift-left testing and continuous testing.
- Experience leading large automation initiatives and technical teams.
- Excellent stakeholder management and communication skills.

Good to Have
- Experience with Palantir or enterprise data/analytics platforms.
- Experience building AI-powered testing accelerators or internal GenAI platforms.
- Experience with LLM evaluation, AI observability or responsible AI practices.
- Knowledge of Agentic AI / AI Agents and LLM-based workflows.
- Experience with cloud platforms beyond AWS.
- Experience with containerization and cloud-native technologies such as Docker and Kubernetes.
- Experience in enterprise-scale digital transformation or engineering modernization programs.
- Relevant certifications in AWS, AI/ML, DevOps or Quality Engineering.

Technology LandscapeGenAI / LLM: GPT-4/4o, Claude, Gemini, Llama, Amazon Bedrock AI Development: GitHub Copilot, Claude, Prompt Engineering

Automation: UI, API, Mobile, Database & End-to-End Automation

Cloud: AWS, Cloud-native Testing

DevOps: CI/CD, Automated Quality Gates, Continuous Testing

Analytics: Quality Analytics, Engineering Metrics, Palantir

Engineering: Automation Frameworks, Test Architecture, Quality Engineering

Leadership ExpectationsThe successful candidate will be expected to:

- Act as a technology and quality engineering thought leader.
- Drive enterprise-wide adoption of automation and GenAI-led testing.
- Balance strategic architecture with hands-on technical leadership.
- Influence senior stakeholders and technology leadership.
- Build and mentor high-performing engineering teams.
- Drive measurable improvements in quality, productivity, automation coverage and release velocity.
- Stay current with rapidly evolving GenAI, LLM and AI-assisted software engineering technologies.

EducationBachelor's or Master's degree in Computer Science, Engineering, Information Technology or a related discipline. What Success Looks LikeWithin the role, success will be measured by the ability to:

- Establish and execute an automation-first quality strategy.
- Increase automation coverage and reduce manual testing effort.
- Introduce measurable productivity gains through GenAI and AI-assisted automation.
- Embed quality gates and continuous testing across CI/CD pipelines.
- Build scalable and reusable automation architectures.
- Improve release quality, speed and engineering efficiency.
- Successfully influence and enable adoption of GenAI-driven Quality Engineering across programs.

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