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About This Role
Requisition ID \# 172494
Job Category: Information Technology
Job Level: Director/Chief
Business Unit: Information Technology
Work Type: Hybrid
Job Location: Oakland
Position Summary
The Chief AI \& Data Architect is accountable for the enterprise‑wide strategy, governance, and value realization of Artificial Intelligence, Advanced Analytics, and Data. This role ensures that data is trusted, governed, reusable, and AI‑ready, and that AI capabilities are deployed safely, compliantly, and at scale across a regulated enterprise. As this is a director level role, this person typically does not own all enterprise AI execution directly, but they orchestrate the strategy, prioritization, standards, and cross\-functional alignment needed to make AI investments produce measurable business outcomes.
The Chief serves as the bridge between data foundations and AI‑driven outcomes, ensuring alignment across business strategy, technology platforms, risk management, and regulatory obligations.
This position is hybrid, working from your remote office and the Oakland General Office Headquarters.
Reporting
Reports into the Senior Director, Enterprise Strategy \& Architecture.
Job Responsibilities
Enterprise AI \& Data Strategy Define and own the integrated AI and Data strategy, roadmap, and operating model aligned with enterprise goals and regulatory commitments. Partner with leaders to prioritize AI and data use cases that deliver measurable value (safety, reliability, efficiency, customer outcomes). Ensure AI investments are grounded in strong data foundations and avoid unmanaged experimentation. Develop the enterprise AI vision, principles, and multi\-year roadmap Align AI priorities to business strategy, growth goals, cost optimization, risk reduction, customer experience, and operational efficiency Identify where AI should be used—and where it should not be used Establish standards across: Generative AI Predictive AI / machine learning Automation / intelligent workflows AI\-enabled analytics and decision support Reduce duplication and fragmentation across AI and analytics efforts.
Data Architecture Serve as owner for enterprise data architecture including developing strategy, standards Ensure data policies, standards, and controls support AI/ML, GenAI, and analytics use cases. Establish standards for Data Products Ensure the enterprise data architecture is fit\-for\-purpose for AI at scale, not just reporting. Define the target\-state data architecture principles to support AI (e.g., data products, data mesh/fabric, feature\-ready data layers) Align data architecture to AI use cases such as: GenAI (context \+ retrieval layers) ML models (training \+ feature pipelines) Real\-time decisioning (streaming architectures) Advocate for architecture patterns that enable: Structured and unstructured data integration Metadata\-driven pipelines High\-quality, reusable datasets for AI Ensure AI strategy is grounded in realistic data capabilities and constraints Define and enforce enterprise data standards that make AI scalable and reusable. Define standards for: Data modeling approaches (e.g., canonical models, domain\-oriented models) Data product design (ownership, SLAs, discoverability) Feature engineering reuse and standardization Metadata and semantic layers to support AI explainability Ensure consistent handling of: structured vs. unstructured data (documents, images, logs, transcripts) embeddings and vector data (for GenAI) Promote “build once, reuse many” data principles
AI Platform, Architecture \& Delivery Own strategy for AI and data platforms, including model lifecycle management, data pipelines, and AI enablement. Ensure AI and data solutions are secure, scalable, auditable, and cost‑effective. Partner with all areas of IT to define reference architectures and approved patterns.
Governance, Risk \& Responsible AI Establish and enforce AI frameworks, including intake, classification, approval gates, and production readiness. Operationalize Responsible AI principles (privacy, transparency, explainability, human oversight). Collaborate closely with Legal, Cybersecurity, Privacy, Compliance, and Risk functions to ensure regulatory alignment.
Executive \& Board Engagement Serve as the enterprise technical authority on AI and Data for executive leadership, regulators, and the Board. Prepare executive recommendations, investment cases, and decision materials Act as a strategic advisor to executives on AI opportunities and implications Translate complex technical topics into clear, decision‑oriented executive insights. Monitor external technology, regulatory, and industry trends to inform strategy. Facilitate alignment across business units and corporate functions Resolve conflicts around priorities, ownership, funding, and standards Lead or support steering committees and leadership forums related to AI
Background Qualifications
Minimum BA/BS degree in Computer Science, Engineering, Business or related field or equivalent experience. 12 years of enterprise architecture experience.
Desired 15\+ years of leadership experience across data, analytics, AI, or enterprise technology. Proven experience delivering enterprise‑scale AI and data programs in complex, regulated environments. Strong understanding of data modeling, cloud platforms, AI/ML lifecycle management, and risk controls. Executive leadership presence with the ability to influence across different lines of business including operations, and IT. MA/MS in Computer Science, Information Systems, Information Security or other Technology Discipline Experience with specific technologies, systems and platforms related to a domain or associated sub\-domain. Experience with hardware, networks, software technologies, applications, and modeling techniques related to a domain or associated sub\-domain. Experience consulting with IT leadership on creating a strategic vision and direction with specific technologies, systems and platforms related to a domain.
Success Measures Measurable enterprise value delivered from AI and analytics. Reduction in ungoverned or duplicative AI initiatives. Increased confidence from all Functional Areas, regulators, auditors, and executives in AI and data practices. AI becomes an enterprise capability, not just isolated experiments
Leadership Qualities
PG\&E expects its leaders to conduct themselves with the highest ethics and integrity and to embody specific leadership qualities.
Strategic Mindset Sees ahead to future possibilities and translates them into breakthrough strategies. Operates effectively, even when things are not certain, or the way forward is not clear.
A Leader in the Community and Industry Effectively builds formal and informal relationship networks inside and outside the organization. Anticipates and balances the needs of multiple stakeholders.
Demonstrates Safety Leadership A safety champion in words and deeds with respect to both employee and public safety. Creating and maintaining a speak up culture free of retaliation.
Influences and Inspires Using various\- communications that convey a clear understanding of the needs of different audiences. Maneuvering comfortably through complex policy, process, and people\-related dynamics.
Optimizes Team Performance Building teams with a strong identity that apply their diverse skills and perspectives to achieve common goals. Creating a climate where people are developed and motivated to do their best to help the organization.
Values Inclusion and Respects Individual Differences Recognizing the value that different perspectives and cultures bring to an organization.
Fiscally Responsible Interpreting and applying understanding of key financial indicators to make better business decisions. Planning and prioritizing work to meet commitments aligned with organizational goals.
Leads Ethically and in a Compliant Manner Sponsoring and sustaining a high integrity speak\-up corporate culture which prioritizes safety, compliance, and ethics. Building on necessary level of industry, company, and subject\-matter expertise, including laws and regulations.
Provides a High Level of Customer Service Building strong customer relationships and delivering hometown, customer\-centric solutions.
Compensation
PG\&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity.
We estimate the successful candidate hired into this role will be placed within the reasonable compensation range of $192,800 to $277,150\. The decision will be made on a case\-by\-case basis. This leadership role is also eligible for an annual Short Term Incentive Plan (STIP) award, as well as the Long Term Incentive Plan (LTIP) grant.
Salary Context
This $192K-$277K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $184K across 1486 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Pacific Gas and Electric, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $175,000 based on 11,128 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $259,350. This role's midpoint ($234K) sits 34% above the category median. Disclosed range: $192K to $277K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Pacific Gas and Electric AI Hiring
Pacific Gas and Electric has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oakland, CA, US. Compensation range: $277K - $277K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (98) are outnumbered by mid-level (1,283) and senior (1,092) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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