Experian is a global data and technology company that drives opportunities for people and businesses around the world. We operate in diverse markets such as financial services, healthcare, automotive, agribusiness, insurance, and more. Experian invests in people and advanced new technologies to unlock the power of data. We have an incredible team of 25,200 employees in 32 countries.
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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.
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.
At Serasa Experian, we believe that diversity is essential for a healthier and more innovative work environment, where everyone can share experiences and express their ideas. That’s why we promote several initiatives to support inclusive recruitment and the professional development of our people.
We also have our affinity groups, created to empower and support individuals from underrepresented groups: ExperianPride (LGBTQIAPN+ community), Ubuntu (racial equity), Women in Experian (gender equity), Aspire (people with disabilities), and Connecting Generations (generations).
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Skills Required
- Experience in machine learning, data science, or software engineering focused on AI safety, alignment, Responsible AI, or model governance
- Strong Python skills
- Proficiency with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or equivalent tools
- Hands-on MLOps experience, including MLflow, Kubeflow, machine learning CI/CD, model monitoring, versioning, and reproducible deployment
- Knowledge of AI safety techniques, including red-teaming, adversarial testing, fairness metrics, interpretability methods, and alignment approaches
- Understanding of AI governance, model risk management, and regulatory expectations in financial services
- Excellent written and verbal English skills, with the ability to explain complex safety concepts to nontechnical audiences
- 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
Experian Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Experian and has not been reviewed or approved by Experian.
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Healthcare Strength — Medical and dental coverage is described as strong, with expanded mental health resources and telemedicine options. Coverage includes inclusive services such as gender transition and fertility support.
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Leave & Time Off Breadth — Time-off offerings are generous, including substantial PTO/vacation, paid holidays, and paid volunteer days with options to purchase additional leave. Parental leave is available for birth and non-birth parents alongside flexible working arrangements that support work-life balance.
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Retirement Support — Retirement programs include a 401(k) with company matching and contributory pension schemes in some regions. These elements complement base pay and bonuses to form a competitive total rewards package.
Experian Insights
What We Do
Experian unlocks the power of data to create opportunities for consumers, businesses and society. During life’s big moments – from buying a home or car, to sending a child to college, to growing a business exponentially by connecting it with new customers – we empower consumers and our clients to manage data with confidence so they can maximize every opportunity. We gather, analyse and process data in ways others can’t. We help individuals take financial control and access financial services, businesses make smarter decision and thrive, lenders lend more responsibly, and organizations prevent identity fraud and crime. For more than 125 years, we’ve helped consumers and clients prosper, and economies and communities flourish – and we’re not done. Our 20,600 people in 43 countries believe the possibilities for you, and our world, are growing. We’re investing in new technologies, talented people and innovation so we can help create a better tomorrow. About Experian: Bringing data to life requires creativity, passion, flexibility and expertise. We want you to share in our success. That's why we offer rewards that recognise great performance. Working in a culture of collaboration, achievement and respect we will give you the support and encouragement you need to develop your skills and talents and progress your career. Everyday our people bring enthusiasm, innovation and inspiration to work and if this sounds like you connect with us at Experian.
