AI Operations Strategist

Job Title: AI Operations Strategist

Job Location: Rosemead, CA

Employment Type: Contract

Job Description: -

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, GCP 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.