Lead the End-to-end AI life cycle in the outage values stream, owning problem framing, data architecture, model development, validation, deployment, monitoring, and continuous improvement
Job Summary
Lead the End-to-end AI life cycle in the outage values stream, owning problem framing, data architecture, model development, validation, deployment, monitoring, and continuous improvement.
Develop high-value outcomes that shapes fleet reliability, outage avoidance, maintenance efficiency, safety, and data compliance, while establishing evolving AI governance, data privacy, cybersecurity, and model risk standards.
Drive rigorous evaluation design, including back testing, user interface reviews, acceptance thresholds, and interpretability standards, while managing model risk through controls and documentation.
Matching Summary
Lead the End-to-end AI life cycle in the outage values stream, owning problem framing, data architecture, model development, validation, deployment, monitoring, and continuous improvement.
Skills & Requirements
Must-have
AI life cycle ownership
data architecture and governance
LLM/RAG architectures
responsible AI frameworks
systems thinking and reusable architecture
Nice-to-have
power generation domain experience
platform technical product ownership
cost/performance trade-offs analysis
familiarity with common tools
Key Requirements
Systems thinking and reusable architecture design
Business acumen to translate targets into technical decisions
Clear communication and influence across stakeholders