Job Description Summary

Job Description

• Lead the End-to-end AI life cycle in the outage values stream: Owns problem framing data architecture meta-Data model development validation deployment monitoring and continuous improvement.
• Combines product analytics along with GFSK (Global Fleet Service Knowledge) architectural ownership (roadmap backlog budget value realization).

• Develop high-value outcomes that shapes fleet reliability outage avoidance maintenance efficiency safety and data compliance.
• Develop evolving AI governance data privacy cybersecurity and model risk standardsdecisions with enterprise-level consequences.

• Lead the SPS data knowledge and bridge this to Industrial standard.
• Own and maintain GSFK AI functionality and align and prioritize with values stream requirements.
• Design sustainable prompting retrieval pipelines guardrails and evaluation framework and benefits.

• Drive rigorous evaluation design: Back testing testing user interface reviews acceptance thresholds and interpretability standards.
• Model risk management: Establishes controls documentation (model cards) and safety-critical and reliability-sensitive use cases.

• Closing data gaps: Orchestrating lineage quality rules imputation/enrichment and upstream remediation across multiple owners is a cross-functional leadership task.
• Own long-lived architecture choices: Schema feature store and pipeline designs lock in costs and agility.

• Aligns Product Engineering Security Compliance and domain SMEs; navigates trade-offs and escalations.
• Supports driving adoption training and process integration across diverse users.

• Reduces iteration cycles.
• Prioritization discipline: Balances quick wins vs. foundational build-out to avoid technical debt and rework.

Require Qualifications

• Systems thinking and reusable architecture design.
• Business acumen to translate SPS value stream targets and goals into technical decisions and measurable outcomes.
• Clear communication and influence across technical and non-technical stakeholders.
• Leadership and mentorship; setting standards and elevating team practices.

Preferred Qualifications

• Experience in power generation or industrial/energy domains.
• Expertise with LLM/RAG architectures prompt governance and evaluation.
• Knowledge of responsible AI and data governance frameworks; model risk management.
• Background in platform/technical product ownership and cost/performance trade-offs
• Familiarity with tools common in this space.

The ideal candidate would demonstrate success through the following metrics.

• Reduced time to onboard new AI use cases via reusable components and standards.
• Deliver platform reliability (uptime) performance (latency/throughput) and cost efficiency.
• Bring to close critical data gaps and improved data quality/maturity.
• Compliance/audit readiness and fewer model risk incidents.
• Demonstrate business impact and bring an adoption across SPS use cases and regions.

Additional Information