Lead Responsible AI Engineer

Integrating these libraries into CI/CD pipelines, developing custom fairness metrics, and tuning hyperparameters for adversarial debiasing models. You'll also be evaluating and approving new open-source tools for enterprise use.

Configuring and building new monitoring dashboards, setting up automated alerting for custom RAI metrics, and architecting experiment tracking for large, cross-functional projects. You'll also lead platform selection for MLOps governance.

Automating the execution of these cloud RAI tools via SDKs, combining their outputs with other libraries for comprehensive reports, and troubleshooting complex configuration issues across our cloud environments.

Defining data governance policies for AI/ML projects, partnering with the Chief Data Officer to implement RAI controls at the data platform level, and writing complex queries for deep bias analysis.

Designing the AI risk module within our GRC platform, creating executive dashboards to report on enterprise AI risk posture, and pulling data for advanced compliance reports.

• Head of Product Management
• Lead Data Scientists
• Legal & Compliance Team
• Engineering Leads
• Internal Audit

• Regulatory Bodies (e.g., ICO)
• Industry Standards Organisations
• External Auditors
• Key Technology Vendors

Synthetic data offers a powerful way to address privacy concerns and data scarcity for bias mitigation, while generative models are also becoming incredibly sophisticated for 'red teaming' – actively trying to break – our own AI systems. This is about using AI to secure AI.

Generative Adversarial Networks (GANs) for Tabular Data · Large Language Models (LLMs) for Adversarial Prompt Generation · Differential Privacy in Synthetic Data Generation · Evaluation Metrics for Synthetic Data Utility & Privacy

• This month: Experiment with `CTGAN` or `SDV` to generate synthetic data from a small, anonymised dataset.
• Next quarter: Research the latest advancements in using LLMs for adversarial testing of vision or NLP models.
• Month 3-6: Propose a pilot project to use synthetic data for training a specific model, evaluating its fairness and privacy benefits.
• Month 6-9: Develop a 'red teaming' strategy using generative AI to proactively identify vulnerabilities in our production models.

Quick win: Use an open-source GAN library to generate a simple synthetic dataset and compare its statistical properties to the real data. See what you can learn.

As AI moves to the edge and data remains decentralised (e.g., on user devices, across partner organisations), traditional centralised governance models break down. Federated Learning offers privacy benefits, but also new governance challenges.

Federated Averaging Algorithms · Secure Multi-Party Computation (SMC) · Decentralised Trust & Auditability · Data Poisoning in Federated Learning

• This month: Read foundational papers on Federated Learning (e.g., Google's original papers).
• Next quarter: Experiment with `PySyft` or `Flower` to set up a basic federated learning simulation.
• Month 3-6: Research the governance implications of federated learning for data privacy and bias detection.
• Month 6-9: Identify a potential internal use case where federated learning could offer a privacy advantage and outline a technical approach.

Quick win: Explore the `TensorFlow Federated` tutorials online. It's a great way to get a feel for how it works.