[Remote] AI Governance & Explainability Engineer

Note: The job is a remote job and is open to candidates in USA. Dice is a company seeking an AI Governance & Explainability Engineer to join their Data Governance team. The role is responsible for ensuring AI solutions are explainable, governable, auditable, and production-ready, embedding governance directly into the AI technology stack and translating policies into technical controls.

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

Skills

• 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
• 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/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
• Experience with AI/ML and GenAI tooling, including Azure AI Foundry/Azure ML
• Experience with ML explainability libraries (e.g., SHAP)
• Experience with LLMs, RAG architecture, and prompt engineering
• 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)

Benefits

• Comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees
• Salaried personnel receive paid time off
• Hourly employees are not eligible for paid time off unless required by law
• Hourly employees on a Service Contract Act project are eligible for paid sick leave

Company Overview

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