AI Governance & Explainability Engineer Edit listing

Requirements
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### Must have:

### - We are looking for 7 years of experience in AI/ML engineering, data science, GenAI/LLMs, NLP, Agentic AI, data governance, or a related field. - We need demonstrated success operationalizing AI governance, explainability, and risk controls in production settings. - We require strong knowledge of Agentic AI architectures and lifecycle considerations. - We value strong analytical and problem-solving skills, especially in risk-based decision making. - We need the ability to drive governance execution initiatives and influence cross-functional partners without direct authority. - We expect excellent organizational skills, attention to detail, and audit-readiness. - Auto insurance or claims experience is preferred. - Experience assessing or governing model training approaches, such as NLP or generative models, without owning full training pipelines is preferred. - Familiarity with synthetic data governance, including generation methods, limitations, and risk documentation is preferred. - Experience with additional AI platforms such as Databricks AI, Snowflake Cortex, or Dataiku is preferred. - Experience in regulated industries, including insurance, financial services, or healthcare, is preferred. - We need strong Python skills and hands-on experience with AI/ML engineering workflows. - Working knowledge of Microsoft Fabric, including Lakehouse, OneLake, notebooks, and pipelines, is required. - Experience with Microsoft Purview, including catalog, lineage, classification, and ownership, is required. - Familiarity with Agentic AI frameworks and patterns such as tool use, planning, and reflection is preferred. - Experience embedding governance controls into CI/CD pipelines using GitHub or Azure DevOps is preferred. - Understanding of cloud platforms, with Azure preferred and AWS or Google Cloud Platform as a plus, is preferred. - Experience preparing audit-ready technical documentation and evidence artifacts is preferred. - Familiarity with reporting and visualization tools such as Power BI for governance and monitoring views is preferred. - Experience with AI/ML and GenAI tooling, including Azure AI Foundry, Azure ML, SHAP, LLMs, RAG architecture, and prompt engineering, is preferred.

Responsibilities:
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- We embed governance, explainability, and risk controls directly into AI, GenAI, and Agentic AI workflows. - We implement governance as code and automation to avoid reliance on manual or retrospective reviews. - We advise solution teams on explainability requirements for automated, semi-automated, and decision-support AI systems. - We ensure human-in-the-loop controls are applied when required by risk level or use case. - We document explainability assumptions, limitations, and residual risk as governance evidence. - We translate enterprise AI policies, standards, and Responsible AI principles into technical guardrails, automated checks, required evidence artifacts, and CI/CD release gates. - We define, generate, and manage explainability outputs tailored to the end-user or reviewer persona and aligned to the decision context and operational use. - We deliver governance engineering with an execution focus rather than policy writing. - We make AI solutions explainable, governable, auditable, and production ready, particularly within the Microsoft Fabric and Purview ecosystem.
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Company:
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We are hiring for a hands-on AI Governance & Explainability Engineer role within our Data Governance team in Tampa, FL. This position focuses on embedding governance directly into our AI technology stack so our AI, GenAI, and Agentic AI solutions are explainable, trustworthy, defensible, and audit-ready in production. We offer comprehensive benefits including medical, dental, and vision coverage, HSA, FSA, 401(k), life, disability, and ADD insurance for eligible employees, and salaried personnel receive paid time off. This role is not eligible for bonuses, incentives, or commissions, and we are proud to be an Equal Opportunity/Affirmative Action Employer.
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