AI Governance & Explainability Engineer

Kforce has a client in Tampa, FL that is seeking an AI Governance & Explainability Engineer. Summary:

• 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

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
• Implement governance as code and automation, eliminating reliance on manual or after-the-fact reviews
• 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
• Document explainability assumptions, limitations, and residual risk as governance evidence

Translate enterprise AI policies, standards, and Responsible AI principles into:

• Technical guardrails
• Automated checks
• Required evidence artifacts
• CI/CD release gates

Define, generate, and manage explainability outputs that are:

• Appropriate to the end-user or reviewer persona
• Aligned to the decision context and operational use
• 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
• Strong analytical and problem solving abilities, particularly in risk based decision making
• 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
• 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)
• 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)
• 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/GCP 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

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