AI Governance & Explainability Engineer

DEPARTMENT: Data Insights and Innovation JOB TITLE: AI Governance & Explainability Engineer JOB CODE: AIGEE REPORTS TO: Data Governance Lead FLSA STATUS: Exempt EMPLOYMENT TYPE: Full-Time JOB PURPOSE: This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion). The AI Governance & Explainability Engineer is a hands‑on technical role within the Data Governance team responsible for ensuring AI, GenAI, and Agentic AI solutions are explainable, governable, auditable, and production‑ready. This role embeds governance directly into the AI technology stack, translating policies, regulatory expectations, and risk requirements into technical controls, automated checks, standardized artifacts, and release gates across the AI lifecycle. The role combines AI/ML engineering depth, GenAI & Agentic AI design knowledge, and governance discipline to ensure AI solutions deliver explainability, can be trusted, defended, and audited in production, particularly within the Microsoft Fabric and Purview ecosystem. DEPARTMENTAL EXPECTATION OF EMPLOYEE Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model Acts as a role model within and outside AF. Performs duties as workload necessitates. Maintains a positive and respectful attitude. Communicates regularly with the departmental leader about department issues. Demonstrates flexible and efficient time management and ability to prioritize workload. Consistently reports to work on time, prepared to perform duties of the position. Meets Department productivity standards. ESSENTIAL DUTIES AND RESPONSIBILITIES AI Governance by Design Engineering (Execution Focus not Policy writing) Embed governance, explainability, and risk controls directly into AI, GenAI, and Agentic AI workflows Translate enterprise AI policies, standards, and Responsible AI principles into: Technical guardrails Automated checks Required evidence artifacts CI/CD release gates Implement governance as code and automation, eliminating reliance on manual or after-the-fact reviews. AI Governance, Explainability & Human Oversight Advise solution teams on explainability requirements for automated, semi-automated, and decision-support AI systems. Ensure human-in-the-loop (HITL) controls are implemented where required by risk level or use case. Define, generate, and manage explainability outputs that are: Appropriate to the end-user or reviewer persona Aligned to the decision context and operational use Document explainability assumptions, limitations, and residual risk as governance evidence. Metadata, Lineage & Governance Evidence Management Operationalize AI Governance in Microsoft Purview by registering and maintaining: AI models, features, prompts, agents, notebooks, and pipelines Maintain end to end lineage across: Data → features → models → inferences → outputs Apply ownership, stewardship, sensitivity, and classification metadata. Ensure governance is maintained Discoverable Versioned Traceable Audit-defensible GenAI & Agentic AI Governance Enablement Apply governance patterns to LLMs, RAG, and Agentic AI solutions Ensure governance traceability when synthetic data or augmented data is used for training, testing, or evaluation. Implement Agentic AI lifecycle governance, including: Observability of agent actions, deviations, and failures Oversight of planning, reflection, and tool-use behavior Controls on autonomous vs. constrained operation Enable GenAI explainability, including: Retrieval transparency for RAG (sources, relevance) Inference context documentation Decision trace generation where applicable Explainability, Interpretability & Model Risk Controls Own and operate explainability capabilities used for governance, audit, and trust. Implement and operationalize techniques such as: Feature attribution (e.g., SHAP or equivalent) Driver and proxy detection