Operations Strategist

AI Operations Strategist

Location: Rosemead, CA
Duration: 6 months


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 showback/chargeback 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 AI/ML 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 ITSM/ITIL, SRE, SLA/SLO/XLA, incident/problem management and operational resilience principles.
•       Strong strategic thinking, executive communication, facilitation and stakeholder-management capabilities.
•       Bachelor's 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, AI/ML, 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.