Job Description
Join FutureTech Innovations as we pioneer the ethical frontier of artificial intelligence for 2026. We seek a visionary AI Ethics & Governance Specialist to shape our responsible AI framework, ensuring our technologies align with human values and regulatory landscapes. This role sits at the intersection of technology, policy, and societal impact, driving initiatives that will define the next generation of AI innovation.
Why FutureTech? We're a leader in quantum computing and neural network development, with a portfolio serving Fortune 500 clients in healthcare, finance, and autonomous systems. Our ethics-first culture offers unparalleled opportunities to influence global standards while working with award-winning researchers and engineers.
Responsibilities
- Design and implement AI ethics frameworks aligned with 2026 regulatory requirements (EU AI Act, NIST standards)
- Conduct bias audits and risk assessments for machine learning models across 50+ production systems
- Develop corporate governance policies for generative AI deployment in regulated industries
- Lead cross-functional workshops with engineers, legal, and product teams on ethical AI integration
- Represent the company in global AI ethics forums and contribute to industry whitepapers
- Monitor emerging AI ethics legislation and adapt compliance strategies proactively
- Mentor junior specialists on ethical AI methodologies and responsible data practices
Qualifications
- Master's degree in Ethics, Philosophy, Computer Science, or related field (PhD preferred)
- 5+ years in AI governance, compliance, or responsible tech development
- Proven experience with bias mitigation techniques in ML pipelines (e.g., IBM AI Fairness 360)
- Deep understanding of global AI regulations (EU AI Act, California Consumer Privacy Act)
- Certification in AI ethics (e.g., IEEE Ethically Aligned Design, Google Responsible AI)
- Published research or policy papers on algorithmic transparency or explainable AI
- Expertise in quantum computing ethics or federated learning privacy paradigms