Sr. Responsible AI Analyst

Sr. Responsible AI Analyst

• Proficiency in Python and ML/DL frameworks (PyTorch, TensorFlow) for evaluation and experimentation. • Understanding of fairness libraries (Fairlearn, AIF360) and ability to compute ethics related evaluation metrics. • Familiarity with explainability tools (SHAP, LIME, Captum, Integrated Gradients). • Exposure to red teaming concepts, prompt safety evaluation, and data integrity checks. • Experience with MLOps basics including evaluation pipelines, experiment tracking, and CI workflows. • Understanding of ML algorithms, generative models, and supervised/unsupervised learning techniques. • Familiarity with NLP, vision, speech, and structured data domains. • Knowledge of datasets, benchmark suites, and third party model ecosystems. 1. Execute Responsible AI Evaluations: Conduct structured assessments on fairness, safety, robustness, explainability, and hallucination risks for models under development or deployment. 2. Design Evaluation Datasets and Metrics: Create curated, adversarial, and edge-case datasets and define quantitative metrics for evaluating fairness, toxicity, reliability, and ethical alignment. 3. Support Guardrail and Control Implementation: Contribute to implementing technical guardrails, safety filters, explainability modules, and automated checks within AI pipelines. 4. Analyze Ethical and Technical Risks: Review datasets, model outputs, and system behavior to identify fairness gaps, robustness issues, transparency deficiencies, and other Responsible AI risks. 5. Participate in Red Teaming and Stress Testing: Assist with scenario-based adversarial evaluations, prompt safety checks, robustness tests, and model vulnerability analysis. 6. Support Deployment of Responsible AI Workflows: Assist in implementing lifecycle governance workflows, templates, and processes—such as via IBM OpenPages or equivalent governance tooling. 7. Prepare Transparency and Governance Documentation: Develop model cards, system cards, evaluation reports, risk logs, and supporting documentation required for governance reviews and audit readiness. 8. Assist in Continuous Monitoring: Support creation of dashboards, metrics, and monitoring signals to track fairness drift, hallucination patterns, model instability, and safety deviations. 9. Collaborate Across Engineering and Governance Functions: Work closely with AI engineers, data scientists, product teams, legal, ISG, DPO, and governance bodies to ensure Responsible AI requirements are consistently applied. 10. Assist in Training and Knowledge Enablement: Help develop training content, guides, and resources to educate internal teams on Responsible AI evaluation methods, guardrails, and governance expectations.