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Information Technology 🏢 Full Time ⭐️ Verified

AI Ethics & Governance Lead

Nexus Dynamics
Austin
Estimated Salary
USD 180.000 – USD 220.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join Nexus Dynamics at the forefront of ethical AI innovation as we shape the responsible future of technology. We're seeking a visionary AI Ethics & Governance Lead to architect frameworks ensuring AI systems align with human values while driving business transformation. In this pivotal role, you'll collaborate with C-suite executives, cross-functional teams, and external stakeholders to embed ethical principles across our AI lifecycle. Your expertise will directly influence our product roadmap, compliance strategy, and corporate responsibility initiatives as we prepare for the 2026 regulatory landscape.

Responsibilities

  • Develop and implement AI ethics governance frameworks aligned with emerging regulations
  • Conduct bias audits and risk assessments for AI deployment models
  • Lead cross-functional working groups on responsible AI practices
  • Advise executive leadership on ethical implications of AI initiatives
  • Establish monitoring protocols for AI system performance and fairness
  • Represent the company in industry coalitions on AI ethics standards
  • Train stakeholders on ethical AI principles and compliance requirements

Qualifications

  • Master's degree in Computer Science, Ethics, Policy, or related field
  • 5+ years in AI governance, ethics, or regulatory compliance
  • Proven experience developing organizational AI ethics frameworks
  • Deep understanding of global AI regulations (EU AI Act, NIST frameworks)
  • Strong analytical skills for bias detection and algorithmic auditing
  • Exceptional communication skills for stakeholder education
  • Certification in AI ethics or governance (e.g., IEEE, BSI)
  • Experience with ML model lifecycle management

Required Skills

AI Ethics Governance Regulatory Compliance Risk Assessment Stakeholder Communication Algorithmic Auditing Policy Development Machine Learning Lifecycle

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