Senior Engineer, AI, Quality Assurance

The job responsibilities outlined in this document are not exhaustive and may evolve over time and be reviewed according to business needs.

ROLE DESCRIPTION SUMMARY

The Senior Engineer – AI Quality Assurance is responsible for designing, driving, and executing the overall AI strategy within QA, establishing AI‑powered quality engineering capabilities, and enabling the organization to adopt and scale AI‑driven testing practices.

This role requires deep expertise in AI/ML technologies, automation frameworks, AI model validation, and quality engineering strategy. The candidate will lead initiatives to embed AI across QA processes—ranging from intelligent test generation and predictive quality analytics to validation of ML models and AI-enabled systems used within SES.

This position will act as the AI anchor for QA, building frameworks, setting governance standards, coaching teams, and partnering with cross-functional groups to ensure AI accelerates high-quality software delivery across the organization.

PRIMARY RESPONSIBILITIES / KEY RESULT AREAS

1. AI Strategy & Roadmap for QA

• Define, build, and maintain the AI strategy for QA, including capability roadmaps and implementation plans.
• Identify high-value AI opportunities across QA workflows such as intelligent test creation, defect clustering, anomaly detection, and quality risk prediction.
• Develop internal AI/ML guidelines, risk considerations, and governance practices specific to QA.

2. AI/ML Engineering for Quality

• Architect, design, and develop AI/ML solutions that improve quality engineering effectiveness and automation maturity.
• Implement models for predictive test selection, failure pattern detection, test suite optimization, and reliability insights.
• Integrate AI capabilities into existing automation pipelines, CI/CD, and QA tools.

3. AI Model Quality Assurance

• Design and execute QA strategies for AI/ML systems including:
• Model performance accuracy validation
• Robustness, fairness, and drift analysis
• Data quality checks and integrity validation
• Explainability and behavioral consistency testing
• Ensure AI/ML components used within SES adhere to reliability, compliance, and ethical AI standards.

4. Intelligent Test Automation

• Build AI-powered enhancements for automation frameworks (e.g., self-healing scripts, auto‑generated test cases).
• Use NLP, LLMs, or ML-based techniques to accelerate functional and non-functional test coverage.
• Implement automation for model validation pipelines (MLOps testing).

5. Collaboration, Mentoring & QA Team Enablement

• Collaborate with QA teams (Functional, Automation, Non‑Functional, Release Team) to introduce and adopt AI capabilities.
• Provide hands-on mentoring, training sessions, and guidance inside QA to uplift AI skills and practices.
• Support QA teams during design reviews, solution evaluations, and strategy discussions related to AI in testing.

6. Reporting, Metrics & Continuous Improvement

• Define measurable KPIs for AI adoption, efficiency gains, quality improvements, and automation impact.
• Produce dashboards and reports showing AI‑driven improvements across QA.
• Continuously refine AI/ML models, pipelines, and tools based on feedback and production results.
• Ensure compliance with SES quality management practices and evolving industry standards.
• Perform other QA-related tasks as required.

COMPETENCIES

• Ability to work in an agile, fast-paced environment and adapt to changing priorities.
• Strategic thinker with the ability to operationalize AI initiatives within QA.
• Strong technical leadership within engineering teams.
• Excellent analytical skills; ability to process complex quality and AI problems.
• High attention to detail, responsibility, and ownership mindset.
• Strong communication skills across distributed global QA teams.
• Adaptability in a rapidly evolving AI technology landscape.
• Proven ability to execute with minimal supervision.
• Self-motivated with a "doer mentality" and willingness to take initiative without supervision.
• Clear and concise written and verbal communication skills.
• Fluency in spoken and written English; additional languages considered an asset.

QUALIFICATIONS & EXPERIENCE

• Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, or related field.
• Several years of QA engineering, automation, or software engineering experience with 3+ years building AI/ML solutions.
• Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit‑Learn).
• Strong programming skills in Python and/or TypeScript/Java, with proven automation experience.
• Experience integrating AI into quality workflows, CI/CD pipelines, or test frameworks.
• Strong understanding of MLOps, ML lifecycle, data pipelines, and model validation.
• Familiarity with Agile methods and QA processes.
• Experience validating ML models for performance, fairness, and drift is a strong advantage.Strong attention to detail, analytical troubleshooting and a commitment to system quality.