Define standards for responsible AI, model governance, and production MLOps.
Build or validate high-risk AI parts and set standards others adopt.
Architect AI systems to prevent systemic failure; lead safety/data incident resolution.
Build reusable AI capabilities: production-ready agents, fine-tuned models, eval methods.
Advise on AI developments worth adopting and shape the domain roadmap.
Serve as senior technical partner to client leadership on AI strategy.

🎯 Requirements

7+ years engineering experience owning AI/ML systems in production.
Experience judging training data quality and balancing model trade-offs.
Experience owning an AI-powered product end to end, including AI behavior.
Established standards such as evaluation methodology, MLOps, or architecture.
Comfort working with executive-level clients and defending positions under pressure.
Deep experience with regulated or on-prem AI deployment, including auditability.

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