****PLEASE NOTE The rates may be negotiable based on experience, education, geographic location, and other factors..****
The AI Operations Strategist is a strategic advisory role within the AI Office under the Digital Transformation Office (DTO). The role shapes the enterprise strategy for reliable, observable, cost-effective and value-driven AI operations
The Strategist defines the enterprise direction for AIOps, AI observability, AI FinOps, operational governance, and value realization. The role establishes target-state capabilities, frameworks, standards, roadmaps, and success measures aligns recommendations across the AI COE, Enterprise Architecture, Cybersecurity, and Information Governance and enables ITSM, SIAM, AIOps, MLOps, Digital Platform, and delivery teams to implement, scale, and measure outcomes.
Key Responsibilities
1. Enterprise AIOps Strategy
Define and evolve the enterprise AIOps vision, strategic principles, target-state capabilities and operating model.
Establish a multi-year AIOps capability roadmap aligned with SCE enterprise priorities and AI transformation objectives.
Assess current-state operational maturity, identify strategic capability gaps and recommend prioritized investments.
Define strategic patterns for predictive operations, anomaly detection, automated remediation and AI-enabled service management.
Guide ITSM, SRE, MLOps, Digital Platform and delivery teams in translating strategy into executable capabilities.
2. AI Observability and Telemetry Strategy
Define the enterprise AI observability and telemetry framework across model, platform, service, experience, risk and business-value dimensions.
Establish minimum observability standards, measurement principles and coverage expectations across the AI lifecycle.
Define enterprise KPIs and KRIs for AI reliability, performance, risk, adoption, cost and business outcomes.
Set strategic requirements for logging, tracing, telemetry, model-performance monitoring and operational insight generation.
Guide architecture, platform, MLOps and SRE teams to align implementation with approved enterprise standards.
3. AI FinOps and Cost Strategy
Define and govern the enterprise FinOps strategy for AI infrastructure, platforms, models, agents and shared AI services.
Establish AI cost-transparency principles, cost taxonomy, unit economics, allocation models and showbackchargeback strategy.
Define cost, consumption, optimization and value KPIs for enterprise AI capabilities.
Establish optimization principles and decision thresholds based on cost, performance, utilization, risk and business value.
Provide strategic forecasting, investment-planning and cost-optimization recommendations in partnership with Finance, Cloud, Platform and delivery teams.
Assess AI FinOps maturity and guide implementation without directly operating cost-management platforms or processes.
4. AI Operations Governance and Standards
Establish AI Operations governance, including decision rights, standards, review mechanisms, escalation principles and accountability boundaries.
Define operational-readiness criteria and governance checkpoints for AI solutions entering production.
Establish strategic standards for reliability, resilience, observability, service management and operational sustainability.
Orchestrate alignment across AI Office, AI COE, Enterprise Architecture, Cyber, Information Governance, ITSM, SIAM, SRE, MLOps, DAS, Digital Platform and Portfolio teams.
Identify ownership gaps, overlaps and dependencies and recommend appropriate governance interventions.
5. Value Realization and Executive Insights
Define the enterprise framework for measuring AI operational value, reliability, cost efficiency, adoption and business outcomes.
Establish standardized KPI, ROI and value-attribution principles across the AI portfolio.
Translate operational, financial and performance insights into strategic recommendations for AI Office and executive stakeholders.
Identify portfolio-level opportunities for investment, optimization, rationalization, reuse or capability improvement.
Provide executive insights on AI operations maturity, emerging risks, cost trends, value realization and strategic priorities.
6. Technology Strategy and Architecture Alignment
Define strategic requirements and evaluation criteria for AIOps, AI observability, telemetry and FinOps capabilities.
Assess technology options based on interoperability, scalability, architecture alignment, governance, cost and business value.
Recommend target-state capabilities and technology-direction principles in partnership with Enterprise Architecture and platform teams.
Promote standardization and reuse while reducing fragmented or overlapping operational capabilities.
Maintain technology neutrality and ensure recommended capabilities align with SCE-approved standards and ecosystem requirements.
7. Cross-Functional Orchestration
Serve as the strategic bridge between the AI Office and architecture, engineering, operations, finance, governance, cybersecurity and delivery stakeholders.
Translate AI strategy into clear operational principles, priorities and requirements for implementation teams.
Facilitate alignment on ownership, dependencies, standards, roadmaps and investment priorities.
Review implementation approaches against approved strategy and recommend corrective direction when required.
Incorporate operational lessons, market developments and stakeholder feedback into the evolving AI operations roadmap.
Role Boundaries
Strategist Accountabilities
Strategy, target state and capability roadmap
Frameworks, policies, principles, standards and guardrails
Portfolio-level KPI, FinOps, observability and value frameworks
Maturity assessment, governance and executive recommendations
Cross-functional orchestration and strategic implementation oversight
Execution Owned by Delivery and Operations Teams
Tool configuration and administration
Telemetry integration, dashboard development and alert configuration
Production monitoring, incident resolution and automated remediation implementation
Cloud cost-management execution, model tuning and retraining
Day-to-day production support and platform operations
Required Qualifications
10+ years of experience across enterprise technology strategy, IT operations transformation, AIOps, SRE, cloud operations, observability or related disciplines.
Demonstrated experience defining enterprise strategies, operating models, governance frameworks, capability roadmaps and transformation programs.
Strong understanding of AIML operations, MLOps, model observability, reliability, telemetry and service management.
Strong understanding of FinOps principles, cloud economics, AI consumption models, cost allocation, forecasting and value optimization.
Experience translating technology, operational and financial insights into executive-level strategic recommendations.
Knowledge of enterprise cloud and AI ecosystems, including Microsoft Azure and Google Cloud Platform.
Knowledge of ITSMITIL, SRE, SLASLOXLA, incidentproblem management and operational resilience principles.
Strong strategic thinking, executive communication, facilitation and stakeholder-management capabilities.
Bachelors degree in Computer Science, Information Technology, Engineering, Data Science, Business Technology or a related discipline.
Preferred Qualifications
Experience defining enterprise AI operating models or AI transformation strategies in a complex, multi-provider environment.
Knowledge of leading AIOps and observability ecosystems such as Dynatrace, Splunk, Datadog, Arize.AI, Azure AI, Google Cloud Platform Vertex AI or equivalent platforms.
FinOps, ITIL4, Cloud, AIML, SRE or enterprise architecture certification.
Familiarity with Agentic AI, enterprise AI platforms, MLOps, Responsible AI and AI lifecycle governance.
Experience developing executive transformation roadmaps and portfolio-level value-realization frameworks.
Utility or energy-sector experience is preferred.
