[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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