Level

Experian

Senior ML Engineer – AI Safety

AI in this role

pytorchtensorflowscikit-learnmlflow
ml-opsai-safety

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.
Our uniqueness is valuing yours. Experian's people-centric, inclusive, and purpose-driven culture is recognized by numerous awards — including World’s Best Workplaces™ 2025 (Fortune's Top 25 global) and Great Place To Work™ in 26 countries, among others. Check out Experian Life on social media or explore our careers website to understand why. Experian is also proud to be an equal opportunity employer and an affirmative action employer. If you have a disability or need that requires accommodation, please let us know as soon as possible.

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. 

  • Advanced English proficiency, with daily interaction with global teams.

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).

Come be part of this transformation!

Experian Careers - Creating a better tomorrow together

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How we rate this

Senior ML Engineer – AI Safety at Experian rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsAI SafetyPyTorchTensorFlowscikit-learnMlflow

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. How do you think about the risk of an AI system in this kind of role failing silently?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. What's a project where you used TensorFlow hands-on?
  5. Walk me through how you've used scikit-learn in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: ML Ops, AI Safety, PyTorch, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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