About The Company

Bridge AI is a pioneering technology company dedicated to transforming the landscape of artificial intelligence and automation solutions. Our mission is to develop innovative AI-driven platforms that empower organizations to achieve unprecedented levels of efficiency, accuracy, and scalability. We specialize in creating intelligent agents, web applications, APIs, and data management systems that seamlessly integrate into diverse business environments.

At Bridge AI, we foster a culture of continuous innovation, collaboration, and excellence, attracting top talent committed to pushing the boundaries of what AI can accomplish. Our commitment to ethical AI development and safety ensures that our solutions not only deliver value but also uphold the highest standards of responsibility and integrity.

About The Role

We are hiring a senior Quality Engineering leader who owns end-to-end quality, release sign-off, and automation strategy across our platform — including AI agents, web applications, APIs, and databases. This role blends quality ownership, automation & agentic testing leadership, release & go-live authority, and product-aligned execution (PO-style ownership). You will start by establishing strong manual and exploratory testing foundations, then systematically drive automation-first quality, including AI agent validation, critic agents, evaluation frameworks, and safety guardrails.

This is not a passive QA role; you are accountable for production quality and ensuring that every release meets the highest standards of reliability and safety.

Qualifications The ideal candidate will have over five years of experience in quality engineering, with a strong background in manual and exploratory testing, automation, and AI-specific validation. Deep understanding of SDLC/STLC, test planning, traceability, and defect management is essential. Proficiency in API testing tools such as Postman or REST clients, UI automation frameworks like Playwright, Selenium, or Cypress, and database validation using SQL is required.





Experience designing and implementing end-to-end automation frameworks, integrating with CI/CD pipelines, and testing LLMs and AI agents is critical.

Strong

Python skills, familiarity with MCP servers, agent tools, orchestration, and cloud platforms (GCP, AWS, Azure) are also necessary. Candidates should demonstrate leadership in testing methodologies, innovation in agentic AI validation, and cross-team collaboration skills.

Responsibilities

End-to-End Quality Ownership &

- Release Sign-Off

- Own quality outcomes across the full delivery lifecycle, ensuring that all features, updates, and releases meet predefined standards.
- Act as the release authority, making go/no-go decisions based on comprehensive validation and testing results.
- Define and enforce quality gates for features, agents, and releases to prevent regressions and ensure stability.
- Collaborate with Product and Engineering teams to balance rapid delivery with high-quality standards.
- Proactively identify and reduce production defects through automation, validation, and continuous improvement initiatives.

Automation-First Quality Strategy

- Define and own the automation roadmap encompassing UI, API, database, and agentic systems testing.
- Build and maintain hybrid automation frameworks using Python, ensuring flexibility and scalability.
- Drive CI/CD integrated automated testing pipelines to facilitate rapid and reliable releases.
- Continuously expand automation coverage to replace manual testing efforts, focusing on repetitive and critical test cases.
- Guide and mentor Quality Engineers in automation implementation, best practices, and innovation.





Agentic AI &

- LLM Quality Engineering

- Own quality assurance for AI agents, tool calls, and multi-step workflows, ensuring protected and reliable operation.
- Validate agent behaviors, reasoning paths, tool invocation correctness, hallucinations, unsafe outputs, and grounding accuracy.
- Define and execute agent evaluation frameworks, including benchmarks and scoring rubrics.
- Collaborate with Critic Engineers to design critic agents, automated validation tools, and evaluation datasets.
- Lead innovation in testing methodologies specific to agentic AI, RAG pipelines, and safety guardrails.

Manual, Exploratory &

- Scenario-Based Testing

- Conduct high-quality manual and exploratory testing to cover scenarios where automation is not yet feasible.
- Design comprehensive test scenarios, including edge cases, failure modes, and real-world flows.
- Implement strong test data strategies and manage defect lifecycle processes effectively.
- Use manual testing strategically to complement automation efforts, ensuring thorough coverage and risk mitigation.

Product-Aligned Ownership

- Partner closely with Product Managers as a quality-focused Product Owner to define acceptance criteria and readiness criteria.
- Participate actively in sprint planning, refinement, and release discussions to embed quality into feature development.
- Ensure quality considerations are integrated into feature design from inception rather than added post-development.

Cross-Team Collaboration

- Work collaboratively with Product, Engineering, AgenticOps, RAG &
- AI teams to ensure cohesive quality standards.
- Act as the single voice on quality across delivery discussions, providing guidance and expertise.
- Mentor and guide Quality Engineers and contributors across teams to foster a culture of quality and continuous improvement.

Benefits Bridge AI offers a competitive benefits package designed to support the well-being and professional

📌 Quality Engineer (India)
🏢 taskverse
📍 India