Securitization QA Engineer with AI experience @ Montreal, QC

Position: Securitization QA Engineer

Location: Montreal, QC

Duration: Contract to Hire

QA Engineer with AI experience, automation, and performance testing. Automation will be Python, Robot Framework, Playwright.

Key Responsibilities

AI Systems and Models Testing

Design and execute comprehensive test strategies for AI systems and models, including prompt engineering, output evaluation, and bias/safety testing. Develop deep understanding of LLM behavior—tokenization, embeddings, attention mechanisms, and inference—to anticipate failure modes. Construct effective prompts, recognize hallucinations and off-target outputs, and assess quality across accuracy, tone, coherence, and bias dimensions. Apply evaluation metrics specific to generative AI and establish appropriate thresholds.

Test AI systems integrated with RAG pipelines and knowledge bases, validating data quality and retrieval accuracy as they impact model outputs. Understand vector database mechanics, similarity search thresholds, embedding drift, and test edge cases including near-duplicate documents, sparse vs. dense embeddings, and performance under scale. Leverage Lang Chain and Lang Graph frameworks to read code, understand chain and graph construction, identify failure points, and write test harnesses. Validate integration points using MCPs, testing tool availability and error handling.

Test Strategy and Planning

Define and execute comprehensive test strategies for securitization platforms, ensuring coverage across functional, regression, integration, and performance testing. Establish testing standards and best practices that span both traditional QA and AI-specific validation.

Test Automation and Framework Development

Design, build, and maintain automated test suites to accelerate release cycles and improve coverage. Leverage AI and ML tools to enhance test coverage, improve efficiency, and reduce regression cycles.

Securitization Lifecycle QA

Validate end-to-end deal workflows including setup, structuring, processing, and distributions. Ensure data integrity across upstream and downstream systems through reconciliation testing and reporting.

Release and Regression Testing

Coordinate regression testing for platform releases, patches, and infrastructure changes. Ensure stability and backward compatibility, particularly during critical processing windows.

Cross-Functional Collaboration

Partner with developers, business analysts, and product owners to clarify requirements, identify edge cases, and ensure testability of new features. Lead defect triage sessions, prioritize issues based on business impact, and maintain clear documentation through to closure.

Quality Leadership and Reporting

Define and monitor key quality indicators including defect density, test coverage, and automation rates. Present findings to leadership and recommend improvements. Mentor junior QA team members and foster a culture of quality across the team.

Production Support

Provide production support during critical processing windows, investigate incidents, and coordinate root cause analysis and remediation efforts.

Qualifications

Experience

7+ years of quality assurance or quality engineering experience, with at least 3 years in a lead or senior capacity. Strong domain knowledge in securitization, capital markets, or similar asset classes.

Technical Skills

Hands-on experience with test automation tools (Selenium, Robot Framework, Playwright, or similar). Proficiency in programming languages including Java and Python, with demonstrated framework implementation expertise. Hands-on API automation and backend system validation experience. Proficiency in database query development, data validation, and reconciliation testing. Experience with CI/CD pipelines and DevOps practices (Jenkins, GitHub, or similar).

AI and ML Competencies

Core understanding of LLM architecture and behavior. Hands-on experience with Lang Chain and/or Lang Graph frameworks. Knowledge of RAG pipelines, vector databases, and agentic solutions. Familiarity with Model Context Protocols (MCPs) and integration testing. Understanding of bias, safety, and red-team testing methodologies for AI systems.

Soft Skills and Mindset

Demonstrated passion for automation, continuous learning, and solving open-ended problems. Excellent analytical, problem-solving, and communication skills. Ability to manage multiple priorities in a fast-paced, deadline-driven environment. Ability to mentor and lead teams while maintaining focus on quality excellence.