About The Company
Bridge AI is a pioneering technology company dedicated to advancing artificial intelligence solutions that transform industries and enhance user experiences. Our platform integrates cutting-edge AI agents, web applications, APIs, and databases to deliver scalable, intelligent, and reliable products. Committed to innovation and excellence, Bridge AI fosters a collaborative environment where talented professionals can thrive and make a meaningful impact in the AI landscape. Our mission is to build intelligent systems that are safe, efficient, and aligned with the needs of our clients and users worldwide.
About The Role
We are seeking a highly experienced senior Quality Engineering leader to own the end-to-end quality assurance process across our comprehensive platform. This pivotal role involves overseeing release sign-offs, defining and executing automation strategies, and ensuring the highest standards of product quality. The ideal candidate will have a deep understanding of manual and exploratory testing, automation frameworks, and AI-specific validation techniques, particularly in the context of large language models (LLMs) and agentic AI systems.
This role blends quality ownership with strategic automation leadership, requiring a proactive approach to testing, validation, and release management. You will work closely with product managers, engineering teams, and AI specialists to embed quality into every stage of development, from initial design to production deployment. Your influence will be instrumental in shaping how we validate AI agents, safety guardrails, and complex workflows, ensuring our products are reliable, safe, and performant.
Starting with establishing robust manual testing foundations, you will drive a shift towards automation-first quality practices, including the development of hybrid automation frameworks, AI agent validation tools, and safety evaluation metrics. This is not a passive QA role but a leadership position that holds accountability for the production quality and safety of our AI-driven solutions.
Qualifications
Ideal candidates will possess over five years of hands-on experience in quality assurance, including extensive manual and exploratory testing. A deep understanding of SDLC and STLC processes, test planning, traceability, defect lifecycle management, and API testing with tools like Postman or REST clients is essential. Demonstrated expertise in UI automation (using Playwright, Selenium, or Cypress), API automation (pytest, requests, REST Assured), and database validation (SQL) is required.
Experience in designing and implementing end-to-end automation frameworks, integrating with CI/CD pipelines (GitHub Actions, Jenkins, GitLab), and working with cloud environments (GCP, AWS, Azure) is highly desirable. Additionally, familiarity with testing LLMs, AI agents, tool invocation workflows, RAG pipelines, and safety validation frameworks is crucial. Strong Python skills for automation utilities and validation, along with knowledge of MCP servers, agent orchestration, and cloud-based testing, will set candidates apart.
Leadership qualities, excellent collaboration skills, and the ability to mentor other quality engineers are important for success in this role.
Responsibilities
End-to-End Quality Ownership & Release Sign-Off
- Own quality outcomes across the entire delivery lifecycle, ensuring products meet defined standards and readiness criteria.
- Act as the release authority, making go/no-go decisions based on comprehensive validation and testing results.
- Define, implement, and enforce quality gates for features, agents, and releases to mitigate risks and ensure stability.
- Partner with product managers and engineering teams to strike the right balance between rapid delivery and product quality.
- Proactively reduce production defects through automation, validation, and rigorous testing practices.
Automation-First Quality Strategy
- Develop and own the automation roadmap, covering UI, API, database, and agentic systems validation.
- Build and maintain hybrid automation frameworks utilizing Python, ensuring scalability and robustness.
- Drive CI/CD integration for automated testing pipelines, enabling continuous validation during development cycles.
- Expand automation coverage to replace manual testing efforts, focusing on repetitive and high-risk scenarios.
- Guide and mentor quality engineers in automation implementation and best practices.
Agentic AI & LLM Quality Engineering
- Take ownership of quality assurance for AI agents, tool calls, and multi-step workflows.
- Validate agent behavior, reasoning paths, tool invocation accuracy, hallucinations, and unsafe outputs.
- Design and execute evaluation frameworks for agent performance and safety, including RAG pipelines and grounding.
- Collaborate with Critic Engineers to develop critic agents, automated validation tools, benchmark datasets, and scoring rubrics.
- Lead innovation in testing methodologies specific to agentic AI systems, ensuring safety and reliability.
Manual, Exploratory & Scenario-Based Testing
- Conduct high-quality manual and exploratory testing where automation is not yet feasible.
- Design comprehensive test scenarios covering edge cases, failure modes, and real-world use cases.
- Implement effective test data strategies and manage defect lifecycle processes.
- Use manual testing strategically to complement automated efforts and ensure thorough coverage.
Product-Aligned Ownership
- Work closely as a quality-focused partner with product owners to define acceptance criteria and readiness.
- Participate actively in sprint planning, backlog refinement, and release discussions.
- Embed quality considerations into feature design and development processes from inception.
Cross-Team Collaboration
- Collaborate effectively with product teams, engineering, AgenticOps, and AI teams to ensure cohesive quality standards.