#22802-CoE - AI -Senior Data Scientist

Date: 19 Jun 2026

Company: QualityAI

Country/Region: IN

Job Description: AI Assurance Engineer About the Role We are seeking an AI Assurance Engineer to help ensure that our AI systems are reliable, safe, compliant, and aligned with ethical and regulatory standards. In this role, you will design and execute testing, validation, monitoring, and governance processes for machine learning and generative AI systems across their full lifecycle. You’ll work closely with data scientists, ML engineers, product teams, security, legal, and compliance stakeholders to build trust in AI by identifying risks, validating performance, detecting bias, and supporting responsible deployment. This is a highly cross-functional role at the intersection of engineering, quality assurance, risk management, and responsible AI. Key Responsibilities AI Validation & Testing Design and execute test strategies for AI/ML models (accuracy, robustness, fairness, explainability, and drift). Perform pre-deployment and post-deployment assurance, including regression testing and continuous monitoring. Develop automated tests for model evaluation and data quality checks. Risk & Compliance Identify and assess AI risks (bias, hallucinations, security vulnerabilities, privacy, model degradation). Support compliance with relevant AI regulations, standards, and internal governance policies. Contribute to model documentation, audit artifacts, and assurance reports. Responsible AI Evaluate models for fairness, transparency, and ethical alignment. Implement bias detection and mitigation techniques. Help define and operationalize responsible AI principles. Monitoring & Observability Build monitoring pipelines for model performance, drift, anomalies, and misuse. Define KPIs and thresholds for AI health. Investigate incidents and coordinate remediation. Collaboration Partner with ML engineers to improve model quality and reliability. Work with product teams to translate business requirements into assurance criteria. Communicate technical findings clearly to non-technical stakeholders. Required Qualifications Bachelor’s or Master’s degree in Computer Science / Data Science, or related field. 3+ years of experience in software QA, ML engineering, data science, or model validation. Strong understanding of machine learning fundamentals and model lifecycle. Experience testing or validating AI/ML systems in production environments. Proficiency in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn). Experience with data analysis and statistical evaluation. Knowledge of bias, fairness, explainability, and model risk concepts. Strong documentation and communication skills. Preferred Qualifications Experience with generative AI and LLM evaluation. Familiarity with AI governance frameworks or regulatory requirements. Experience building automated test pipelines and monitoring dashboards. Certifications or coursework in responsible AI, ML ops, or quality engineering.