Senior ML Engineer – AI Safety

Job description

We are looking for an experienced, proactive Senior ML Engineer to join Experian’s AI Safety team. You will lead the design, implementation, and governance of safety-critical GenAI systems across the business, partnering with senior engineers, security, risk, compliance, legal, and product stakeholders to ensure AI systems are robust, explainable, fair, auditable, and aligned with regulatory and ethical standards. As a senior contributor, you will mentor engineers, shape technical strategy, and help drive adoption of AI safety best practices across the organisation.

Key responsibilities Lead the design and implementation of Responsible AI frameworks, governance policies, and safety guardrails for GenAI systems. Define and own AI safety evaluation pipelines, including red-teaming, adversarial robustness testing, jailbreak and prompt injection assessments, and automated safety benchmarks. Develop explainability and interpretability tooling to support model audits, regulatory reviews, and clear communication of model behaviour and limitations.

Partner with risk, compliance, legal, privacy, security, product, and engineering teams to embed safety requirements into scalable GenAI solutions. Lead incident response and root cause analysis for AI-related safety issues, including post-incident reviews and remediation playbooks. Contribute to GenAI-powered solutions in fraud detection, credit risk, customer service automation, and platform initiatives while ensuring alignment with AI risk appetite.

Mentor junior and mid-level engineers and represent AI Safety in cross-functional forums.

Qualifications

Required qualifications Experience in machine learning, data science, or software engineering, including focused on AI safety, alignment, Responsible AI, or model governance. Strong Python skills and proficiency with ML frameworks such as TensorFlow, PyTorch, scikit-learn, or equivalent tooling. Hands-on MLOps experience, including MLflow, Kubeflow, CI/CD for ML, model monitoring, versioning, and reproducible deployment practices.

Demonstrated knowledge of AI safety techniques including red-teaming, adversarial testing, fairness metrics, interpretability methods, and alignment approaches. Strong understanding of AI governance, model risk management, and regulatory expectations in financial services, with practical experience preparing documentation for audit or regulatory review preferred. Excellent written and verbal English skills, with the ability to translate complex safety concepts for non-technical audiences.

Nice-to-have Experience designing or running automated benchmark suites for LLMs or other GenAI systems. Familiarity with bias detection, harm classification, content safety tooling, or policy evaluation frameworks. Experience with regulated financial services use cases such as fraud detection, credit risk, customer service automation, or model risk management. Experience influencing engineering standards or mentoring engineers in AI safety, Responsible AI, or production ML practices.