Founding QA Engineer (AI-Native)

Manif AI; Bengaluru; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

Manif AI in Bengaluru, India, is seeking a Founding QA Lead to build a modern, AI-native quality engineering organization from the ground up. This is not a traditional manual QA role; AI will be embedded throughout the development lifecycle.

You will define how quality is engineered across the company, building automated validation platforms, CI/CD pipelines, and test tooling to enable rapid, reliable shipping and continuous improvement.

Qualifications

• Deep technical foundation in computer science, mathematics, engineering, or related quantitative discipline.
• Significant experience building automated testing platforms for distributed systems rather than manual testing.
• Strong programming ability (JavaScript/TypeScript, Python, or similar) and ability to build internal tools.
• Experience testing APIs, distributed systems, databases, authentication systems, and cloud-native applications.
• Strong understanding of CI/CD, observability, production monitoring, and release engineering.
• Familiarity with modern testing approaches including property-based testing, fuzz testing, contract testing, mutation testing, and synthetic test data generation.
• Experience with payment systems, financial infrastructure, or other high-reliability distributed systems is highly desirable.

Responsibilities

• Define and lead QA across the company by building a modern automated validation platform.
• Develop automated API, integration, end-to-end, and regression test frameworks.
• Enable AI-assisted test generation from specifications and product requirements.
• Create CI/CD integrated validation pipelines and infrastructure for performance, load, and resilience testing.
• Ensure security and compliance validation for financial systems.
• Establish production monitoring and automated regression detection.

Skills

Deep technical foundation

Automated testing platforms

JS/TS & Python

API testing

CI/CD & observability

Modern testing approaches

Payments/financial infra

Job description

Build the quality infrastructure that enables AI agents to transact globally.

AI is changing how software is built. We believe it will fundamentally change how commerce works.

Millions of AI agents will soon discover products, negotiate prices, make purchases, and manage financial workflows on behalf of consumers and businesses. Existing payments infrastructure wasn't designed for autonomous software.

Manif is building the infrastructure and control plane for agentic commerce—a neutral layer that enables AI agents to transact safely across payment providers, protocols, regions, and payment methods while ensuring consumers and merchants remain in control.

We are currently operating in stealth and are well funded by leading venture investors and strategic partners. With fundraising behind us, our full attention is on building the infrastructure that will power the next generation of global commerce.

The Role

Quality is not a function at Manif—it is a core engineering discipline.

We are looking for aFounding QA Leadto build a modern, AI-native quality engineering organization from the ground up. This is not a traditional manual QA role. We expect software to be specified, implemented, tested, and validated with AI deeply embedded throughout the development lifecycle.

You will define how quality is engineered across the company by building a continuous, automated validation platform that gives us confidence to ship quickly without compromising reliability.

You will work closely with the founding engineering team to build:

• Automated API, integration, end-to-end, and regression test frameworks
• AI-assisted test generation from specifications and product requirements
• Continuous validation pipelines integrated into CI/CD
• Performance, load, resilience, and chaos testing infrastructure
• Security and compliance validation for financial systems
• Test environments, synthetic data generation, and developer tooling
• Production monitoring and automated regression detection

Rather than validating software after it is written, you'll help define a development process where quality is continuously verified from specification through production.

Qualifications

We're looking for someone who views quality engineering as a software engineering discipline.

Successful candidates will typically have:

• A deep technical foundation in computer science, mathematics, engineering, or a related quantitative discipline.
• Significant experience building automated testing platforms for distributed systems rather than primarily performing manual testing.
• Strong programming ability (JavaScript/TypeScript, Python, or similar) and the ability to build internal engineering tools.
• Experience testing APIs, distributed systems, databases, authentication systems, and cloud-native applications.
• Strong understanding of CI/CD, observability, production monitoring, and release engineering.
• Familiarity with modern testing approaches including property-based testing, fuzz testing, contract testing, mutation testing, and synthetic test data generation.
• Experience with payment systems, financial infrastructure, or other high-reliability distributed systems is highly desirable.
  

You’ll Probably Thrive Here If You...

• Already use AI as a core part of your daily engineering workflow.
• Believe the future of QA is continuous, automated, and AI-assisted—not manual test execution.
• Think like a software engineer who happens to specialize in quality.
• Can build frameworks that thousands of tests run on—not simply write individual test cases.
• Sweat the details that matter: correctness, determinism, idempotency, security, resilience, and edge cases.
• Enjoy designing systems that prevent defects instead of merely finding them.
• Have high agency, enjoy ambiguity, and are excited about creating engineering practices from first principles.

Our Philosophy

Quality is our identity.

Quality is not something we inspect into software—it is something we design into the system. Every specification, architecture decision, test, and deployment contributes to long-term reliability.

Users above all.

We measure ourselves by the trust our platform earns.

High agency. Low ego.

We communicate directly, challenge ideas respectfully, and optimize for the best solution.

Design creates leverage.

As AI increasingly generates code, engineering leverage shifts toward specifications, architecture, validation, and systems thinking.

AI-native by default.

AI is embedded throughout our engineering workflow. Humans remain accountable for design, judgment, and stewardship of outcomes.

If you're excited about redefining how software quality is engineered in an AI-native world—and helping build the infrastructure behind the next generation of global commerce—we'd love to talk.