AI Governance Lead

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading organisations across Australia and the Asia-Pacific region, helping them navigate the opportunities and risks of AI adoption — with rigour, transparency, and trust. With the support of IBM's global Trustworthy AI Centre of Excellence, world-class research capabilities, and an unmatched portfolio of AI governance tooling, you will be at the forefront of responsible AI in Australia. At IBM Consulting, intellectual curiosity is a superpower. You'll be encouraged to explore the boundaries of what responsible AI means — technically, ethically, and legally — and to translate that knowledge into practical frameworks that our clients can act on. This role sits at the intersection of AI engineering, research, and policy: a rare opportunity for a technically deep practitioner who also wants to shape the future of responsible AI in Australia and beyond. The AI Governance Lead will serve as IBM Consulting Australia's principal technical authority on trustworthy, responsible, and explainable AI. You will lead the AI Governance practice, advising strategic clients on how to build, deploy, and operate AI systems that are explainable, fair, transparent, and compliant with emerging Australian and global regulation. You will work as a hands-on technical leader and thought partner — able to get deep into the mechanics of a model and equally comfortable presenting to a senior executive audience. Your primary responsibilities will include: •      Lead IBM Consulting Australia's AI Governance practice, building technical frameworks and client offerings that address explainability, fairness, transparency, and accountability across the AI lifecycle. •      Design and implement model explainability solutions — including post-hoc explanation methods (SHAP, LIME, counterfactual explanations), intrinsically interpretable architectures, and causal inference-based approaches — tailored to regulated industry contexts. •      Architect AI fairness and bias detection pipelines: audit datasets and model outputs for demographic parity, equalized odds, and disparate impact; design remediation strategies aligned to client risk appetite. •      Develop and operationalise AI governance frameworks — risk taxonomies, model risk management processes, AI impact assessments, and monitoring dashboards — leveraging IBM watsonx.governance and open-source tooling. •      Translate the rapidly evolving global AI regulatory landscape (EU AI Act, Australia's Voluntary AI Safety Standard and guardrails for high-risk AI, NIST AI RMF, ISO/IEC 42001) into practical, industry-specific compliance postures for Australian clients across financial services, healthcare, government, and energy. •      Conduct applied AI governance research: monitor academic literature, regulatory developments, and industry standards; synthesise findings into client-ready thought leadership, white papers, and practice assets. •      Partner with AI engineering teams to embed governance by design — integrating fairness constraints, explanation generation, and audit logging into MLOps pipelines and model deployment workflows. •      Serve as a trusted advisor and subject matter expert in client engagements, RFP responses, and executive briefings — articulating complex AI governance concepts clearly to both technical and non-technical audiences. •      Contribute to and represent IBM at external forums, industry bodies, standards committees, and academic partnerships relevant to responsible AI in Australia. •      Mentor junior practitioners and data scientists in responsible AI methods, building internal capability and a culture of governance-first AI development. •      Demonstrated depth in AI/ML engineering or AI research — you have built, trained, and evaluated machine learning models in production or research settings and can reason rigorously about their internals. •      Hands-on expertise in model explainability and interpretability techniques (e.g., SHAP, LIME, integrated gradients, counterfactual explanations, attention mechanisms, causal inference) and the ability to select the right method for the right context. •      Practical experience designing and implementing AI fairness assessments: familiarity with bias metrics, protected attribute analysis, and mitigation techniques (pre-, in-, and post-processing). •      Strong grounding in responsible AI principles — transparency, accountability, safety, privacy, and human oversight — and their application within an enterprise or regulated environment. •      Proven ability to independently research and rapidly assimilate new technical and policy developments; a track record of turning emerging knowledge into actionable guidance. •      Proficiency in Python and the core ML/data science stack (scikit-learn, PyTorch or TensorFlow, pandas, Jupyter); familiarity with MLOps tooling and model monitoring platforms. •      Excellent written and verbal communication skills — the ability to write precisely and persuasively for technical audiences and to simplify complex ideas for executives, regulators, and clients. •      Experience leading or significantly contributing to client-facing consulting engagements, research projects, or cross-functional technical programmes. •      Awareness of and engagement with the global AI regulatory landscape, including the EU AI Act, Australia's Voluntary AI Safety Standard, NIST AI RMF 1.0, ISO/IEC 42001, and sector-specific guidance from APRA, ASIC, or the TGA. •      Understanding of how AI regulatory requirements differ across high-risk industries — financial services, healthcare, critical infrastructure, government — and the practical compliance implications for model development and deployment. •      Experience with causal inference methods (propensity scoring, instrumental variables, difference-in-differences, structural causal models) and their application to AI fairness or impact evaluation. •      Familiarity with IBM watsonx.governance, IBM AI Fairness 360, IBM AI Explainability 360, or equivalent open-source toolkits (e.g., Fairlearn, InterpretML). •      Published research, technical blog posts, or conference presentations in AI, machine learning, responsible AI, or a related field — demonstrating both research rigour and a commitment to knowledge sharing. •      Experience in a research environment (academia, industrial research lab) and the ability to bridge the gap between research and applied consulting. •      Domain experience in regulated industries such as healthcare, financial services, insurance, government, or energy. Australia Consulting Professional SYDNEY, AU SYDNEY (0008) IBM Australia Limited Application-Specific Integrated Circuits Artificial Intelligence Bridge Consulting Data Science Energy Engineering Financial Services Government Healthcare Infrastructure Insurance Knowledge Sharing Machine Learning Mitigation National Institute of Standards and Technology Presentations Python Regulatory Requirements Research Accountability Jupyter Notebook Commitment Risk management framework Transparency Compliance Pandas Library PyTorch Scikit-learn Tensorflow Artificial Intelligence Research Rigor Governance Safety IEC Standards International Organization for Standardization Attention Mechanisms Bias Blog Posts Causal Inference European Union Artificial Intelligence Act Machine Learning Operations Verbal Communication Tooling