AI Governance & Explainability Engineer / Remote

AI Governance & Explainability Engineer / Remote

 

JOB PURPOSE:

• This role 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.

 

 

 

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

• Global and local model explanations

• Identify bias signals, risk indicators, and explainability gaps.

• Store and manage explainability and observability outputs as governed, audit-ready

artifacts.

• Support audit, compliance, and risk review activities with defensible evidence.

• Monitoring, Observability & Incident Readiness

• Define and implement AI monitoring metrics, alerts, and thresholds for:

• Performance degradation

• Bias and ethical risk indicators

• Drift and instability.

• Partner with MLOps and platform teams to integrate monitoring into production

pipelines.

• Support AI incident response and post-incident reviews with governance evidence.

• Ensure all observability outputs are retained, traceable, and audit ready.

• Governance Checkpoints & Release Gating

• Define and enforce governance checkpoints within CI/CD pipelines (DEV-> TEST/UAT -> PROD).

• Implement automated release checks for:

• Required documentation and evidence artifacts.

• Explainability artifacts

• Monitoring configuration

• Data usage, lineage completeness, and medallion-layer alignment

• Partner with Engineering and MLOps teams on promotion decisions while owning

governance readiness, not platform approval.

 

 

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, Data

Science, Engineering, or a related field.

• Minimum 7 years of experience in AI/ML engineering, data science, GenAI/LLMs, NLP,

Agentic AI, data governance, or related roles.

• Demonstrated experience operationalizing AI governance, explainability, and risk

controls in production environments.

• Deep understanding of Agentic AI architectures and lifecycle considerations.

 

 

Technical Skills

• Strong proficiency in Python with hands-on experience in AI/ML engineering

workflows.

• Working knowledge of Microsoft Fabric (Lakehouse, OneLake, notebooks, pipelines).

• Experience with Microsoft Purview (catalog, lineage, classification, ownership).

• Experience with AI/ML and GenAI tooling, including Azure AI Foundry / Azure ML

• ML explainability libraries (e.g., SHAP) LLMs, RAG architecture, and prompt

engineering

• Familiarity with Agentic AI frameworks and patterns (e.g., tool use, planning,

reflection).

• Experience integrating governance controls into CI/CD pipelines using GitHub or

Azure DevOps.

• Understanding of cloud platforms (Azure preferred; AWS/Google Cloud Platform a plus

• Experience producing audit-ready technical documentation and evidence artifacts.

• Familiarity with reporting and visualization tools (e.g., Power BI) for governance and

monitoring views.

 

 

Soft Skills

• Strong analytical and problem-solving abilities, particularly in risk-based decision

making. Excellent written and verbal communication skills, with the ability to

translate technical details into governance-relevant insights.

• Ability to lead governance execution initiatives and influence cross-functional teams

without direct authority.

• Strong organizational skills with attention to detail and audit readiness.

• Auto insurance or claims industry experience preferred.

 

 

Preferred Qualifications

• Experience evaluating or governing model training approaches (e.g., NLP, generative

models) without owning full training pipelines.

• Familiarity with synthetic data governance (generation methods, limitations, risk

documentation).

• Experience with additional AI platforms (Databricks AI, Snowflake Cortex, Dataiku).

• Experience in regulated industries (insurance, financial services, healthcare).