Quality Engineer

About The Company

Bridge AI is a pioneering technology company dedicated to advancing artificial intelligence solutions that empower businesses and individuals alike. Our innovative platform integrates AI agents, web applications, APIs, and databases to deliver seamless, intelligent experiences. Committed to pushing the boundaries of AI capabilities, Bridge AI fosters a collaborative environment where creativity, technical excellence, and continuous learning drive our success. Our team comprises passionate professionals who are dedicated to shaping the future of AI technology and making a meaningful impact across various industries.

About The Role

We are seeking a highly experienced and strategic senior Quality Engineering leader to take ownership of end-to-end quality assurance, release sign-off, and automation strategy across our diverse platform ecosystem. This pivotal role involves overseeing the quality of AI agents, web applications, APIs, and databases, ensuring that our products meet the highest standards of reliability, safety, and performance. The ideal candidate will have a proven track record in leading quality initiatives, driving automation-first approaches, and managing complex testing frameworks, particularly within AI and agentic systems. This role blends quality ownership, automation leadership, release authority, and product-aligned execution. You will start by establishing robust manual and exploratory testing foundations, then systematically drive automation initiatives—including AI agent validation, critic agents, evaluation frameworks, and safety guardrails. Your leadership will influence the entire product lifecycle, ensuring that quality is embedded into every stage from development to deployment. This is not a passive QA role; it requires proactive engagement, strategic thinking, and accountability for production quality outcomes.

Qualifications

The ideal candidate will possess a minimum of 5+ years of hands-on experience in quality engineering, with a strong background in manual and exploratory testing. Deep understanding of SDLC/STLC, test planning, traceability, and defect management is essential. Candidates should have extensive API testing experience using tools such as Postman or REST clients, along with proficiency in automation frameworks and scripting languages.

Expertise in UI automation (Playwright, Selenium, Cypress), API automation (pytest, requests, REST Assured), and database validation using SQL is required. Experience designing and maintaining end-to-end automation frameworks, along with CI/CD pipeline integration (GitHub Actions, GitLab, Jenkins), is highly valued. Familiarity with testing LLMs and AI agents—including understanding agent workflows, tool invocation, RAG pipelines, and safety validation—is crucial.

Strong Python skills for automation, utilities, and validation are necessary, as well as knowledge of MCP servers, agent tools, and orchestration. Cloud exposure in GCP, AWS, or Azure within QA or automation contexts will be advantageous. Candidates should demonstrate a passion for innovation in AI testing methodologies and possess excellent collaboration skills to work effectively across teams.

Responsibilities

End-to-End Quality Ownership & Release Sign-Off

• Own quality outcomes across the full delivery lifecycle, ensuring products meet defined standards before release.
• Act as the release authority, making go/no-go decisions based on comprehensive validation and testing results.
• Define and enforce quality gates across features, AI agents, and release cycles to mitigate risks.
• Collaborate with Product and Engineering teams to balance development speed with quality assurance.
• Proactively reduce production defects through automation, validation, and rigorous testing strategies.

Automation-First Quality Strategy

• Define and execute the automation roadmap covering UI, API, database, and agentic systems.
• Build and maintain hybrid automation frameworks utilizing Python and modern testing tools.
• Drive CI/CD-integrated automated testing pipelines to ensure continuous quality delivery.
• Expand automation coverage to replace repetitive manual testing, increasing efficiency and reliability.
• 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 correctness, and hallucinations or unsafe outputs.
• Define and implement agent evaluation frameworks, including benchmarks and scoring rubrics.
• Collaborate with Critic Engineers to design critic agents, automated validation, and ground-truth datasets.
• Lead innovation in agent testing methodologies to improve safety, robustness, and grounding accuracy.

Manual, Exploratory & Scenario-Based Testing

• Drive high-quality manual and exploratory testing efforts where automation is not yet feasible.
• Design comprehensive test scenarios covering edge cases, failure modes, and real-world scenarios.
• Develop strong test data strategies and oversee defect lifecycle management.
• Utilize manual testing strategically to complement automation efforts, ensuring thorough coverage.

Product-Aligned Ownership

• Partner with Product Owners to align testing and quality criteria with product acceptance standards.
• Participate actively in sprint planning, refinement, and release discussions to embed quality into feature design.
• Ensure that quality considerations are integrated early in the development process rather than added post-development.

Cross-Team Collaboration

• Work closely with Product, Engineering, AgenticOps, RAG, and AI teams to ensure cohesive quality strategies.
• Act as the single quality voice across delivery discussions