AI Quality and Automation Engineer

Nenu AI; Chennai District; On-site; INR 1,400,000 - 2,400,000; Full time

Job summary

Nenu AI, a Chennai-based AI-first startup, seeks an AI Quality & Automation Engineer with 4-7 years of experience to redefine quality in an AI-first engineering org. You will own end-to-end quality, build scalable testing environments, and drive best practices across the lifecycle.

You will apply AI in test generation, analysis, and release validation, shaping the 0-1 quality framework from ground up and ensuring reliability before production."

Qualifications

• 4-8 years of experience in test automation or QA engineering.
• Ability to break down complex systems and identify edge cases.
• Hands-on with automation frameworks end-to-end.

Responsibilities

• Own quality end-to-end across development, testing, and deployment.
• Build the quality framework from 0-1 tailored to the product.
• Drive testing best practices and continuous improvement.
• Use AI across testing lifecycle for generation, validation and analysis.

Skills

Test automation

QA engineering

AI in testing

CI/CD

Debugging

Tools

Claude Code

Grok

Codex

Job description

Nenu is a fast-moving, Silicon Valley-based, VC-backed startup building ambitious, user-facing products at the intersection of modern web applications and AI-powered systems.

From the heart of Chennai, we're building for global scale - focused on speed, ownership, and creating high-impact products from the ground up.

We're at a stage where the foundations are being built and scaled, which means the people who join now will directly shape how the product, platform, and engineering practices evolve.

We are an AI-first company, using AI not just in our products, but across how we build, test, deploy, and operate.

About the Role

We're looking for an AI Quality & Automation Engineer with 4-7 years of experience who thinks beyond traditional QA and wants to redefine how quality is built in an AI-first engineering organization.

You will own quality end-to-end — not just writing test scripts, but building the systems, frameworks, practices, and intelligence that help the entire engineering team ship better software with confidence.

This is a 0-1 role. You'll build our quality and automation approach from the ground up, based on how our actual product, systems, and engineering workflows work. You'll use AI throughout the lifecycle to test, validate, analyze, and continuously improve how we build software.

You'll also be responsible for continuously evolving how we test — bringing in new ideas, tools, techniques, and industry best practices and turning the ones that work into strong engineering practices across the organization.

In simple terms, you are the person who makes sure what we build actually works, and builds systems to catch what could go wrong before it reaches production.

You'll be:

• Building the quality and automation framework from 0-1
• Testing and validating across the entire product lifecycle
• Using AI throughout the testing and validation lifecycle
• Finding problems before they become production problems
• Continuously improving how the organization approaches quality

Core Competencies — What You Will Do

• Own quality end-to-end: Take responsibility for product quality across development, testing, and deployment - not just individual test cases or automation suites.
• Build the quality framework from 0-1: Design and build a strong, scalable testing environment based on our actual product, systems, workflows, and failure modes - not a generic testing template.
• Drive testing best practices: Establish and continuously evolve best practices across test design, automation, coverage, validation, regression prevention, reliability, and release quality.
• Continuously improve the testing practice: Stay curious about emerging testing approaches, AI capabilities, tools, and industry practices; identify what can make our quality process better and actively implement what is valuable.
• Bring learning into the organization: Experiment with new tools, techniques, and approaches, turn useful learnings into repeatable practices, and help the broader engineering team adopt them.
• Build proactive quality systems: Think beyond testing what already exists. Identify how systems can fail, anticipate edge cases, and build checks that catch problems before they reach production.
• AI-driven testing: Use AI throughout the testing lifecycle - from understanding changes and generating test scenarios to writing tests, debugging failures, identifying gaps, and validating releases.
• Validate AI-generated development: Build mechanisms to independently validate code, features, and system changes produced with AI so that development speed does not come at the cost of reliability.
• Build intelligent automation: Create automation that is reliable, adaptive, maintainable, and capable of evolving as the product and engineering practices change.
• Own deployment quality: Integrate automated validation into development and deployment workflows so changes are continuously tested before and after release.
• Find what others don’t: Go beyond expected test scenarios, challenge assumptions, and identify edge cases, failure modes, and gaps that traditional testing approaches may miss.
• Raise the engineering quality bar: Make quality a shared engineering value by partnering with engineering and product teams and influencing how quality is built into the development lifecycle.
• Challenge existing approaches: Question how testing is normally done, identify better ways of working, and introduce approaches that improve speed, coverage, confidence, and reliability.
• Influence without authority: Bring clarity, evidence, and practical solutions that help the broader engineering team adopt stronger quality practices.

Skills & Capability Stack — What You Need to Succeed

• 4-8 years of experience in test automation, quality engineering, software testing, or a closely related engineering role
• Strong logical and analytical thinking with the ability to break down complex systems and problems
• Strong understanding of how software can fail and the ability to proactively identify edge cases and failure modes
• Hands-on experience building or owning automation frameworks end-to-end
• Deeply hands-on with AI development and engineering tools such as Claude Code, Grok, Codex, and similar tools
• Ability to use AI for test generation, code generation, debugging, analysis, refactoring, coverage improvement, and problem solving
• Ability to understand and reason across different layers of a software system
• Understanding of deployment workflows, CI/CD, release processes, and production environments
• Ability to work across multiple frameworks, languages, and tools based on what the product requires
• Strong debugging skills and the ability to trace issues across multiple layers of a system
• Ability to design testing approaches based on the specific product and system rather than relying on predefined templates
• Curiosity to continuously learn new tools, technologies, and testing approaches — and the ability to turn useful learning into practical improvements
• Ability to influence engineers and teams through technical clarity, collaboration, and strong reasoning
• Strong ownership mindset with the ability to build a quality practice from the ground up
• Ability to balance speed, coverage, reliability, and maintainability in a fast-moving environment

Tech Environment — What You’ll Work With

• Grok
• Codex
• AI-assisted development and testing tools
• AI-powered automation
• Emerging AI testing and quality tools
• Automation frameworks and tools that best fit the product and problem

You are not expected to come in with a fixed toolbox. The expectation is that you can learn, adapt, experiment, and use the right tools to solve the quality challenges we face.

What Makes You a Great Fit

• You don’t think of yourself as a traditional QA tester — you think like an engineer who owns quality.
• You enjoy building things from scratch and are comfortable with 0-1 problems.
• You naturally think about what could go wrong, not just whether the expected flow works.
• You are deeply curious about AI and already use AI tools as part of how you work.
• You don’t just learn new tools or practices — you experiment with them and put the valuable ones into practice.
• You're