Job Responsibilities
Architect, build, and scale the operational frameworks, architectural patterns, and data pipelines needed to control AI costs, optimize token usage, and enforce governance
Design and implements complex architectural solutions using cloud design principles, best practices and industry standards
Standardize Consumption: Build the enterprise-wide framework for tracking LLM token consumption across AWS (Amazon Bedrock, SageMaker) and Azure (Azure OpenAI, AI Studio).
Implement Guardrails: Define and implement real-time rate limiting, token throttling, and multi-cloud model-routing policies to control costs before they occur.
Establish Unit Economics: Define AI-specific unit metrics (e.g., cost-per-successful-inference, revenue-per-million-tokens) to measure true business ROI.
Leads unique and complex cost/benefit modelling with mapping to business KPI's
Educates team to design and implement architectures based on FinOps best practices
Provides hands-on technical coaching to accelerate Cloud learning across the Cloud Center of Excellence (CCOE)
Ensures that the design, development and delivery of infrastructure in the Cloud incorporates FinOps principals, to ensure continued cost optimization.
Develops strategies and steps to incorporate FinOps data and practices into the development lifecycle.
Collaborate with Cloud Service Providers and FinOps tool vendors to mature overall FinOps capabilities and drive further adoption of the FinOps practice by implementing solutions that enable cost optimization and shift work left.
Designs and develops data pipelines to ingest, process, and transform data from various sources into actionable insights for both AWS and Azure CSPs.
Hands on configuration and administration of all FinOps Tooling and implementation of new tooling, including integration with Azure and AWS Cloud platforms as applicable.
Develop scripts and tools for automating deployment, monitoring, and scaling processes.
Consult with incoming and onboarded application and business teams, to ensure their solutions are defined with FinOps principals, to optimize cost; work with them to close any gaps.
Assist in defining additional technical FinOps roles needed to deliver solutions, which may include reviewing profiles, helping interview and helping select candidates.
Optimize and fine-tune data workflows to ensure high performance and reliability.
Collaborate with cross-functional teams to understand data requirements and translate them into scalable and efficient data solutions.
Implement security measures and data governance best practices to maintain data integrity and compliance standards.
Troubleshoot and resolve data-related issues in a timely manner.
Consult in defining and maintaining a FinOps tagging policy to ensure scalability and support accurate cost attribution
Consult in identifying and tracking metrics and KPIs to assess FinOps performance and identify optimization opportunities
Ensure compliance with relevant industry standards and financial regulations.
Automate workflows to streamline cost management processes, including anomaly detection and billing adjustments.
Work with engineering teams to automate operational cost optimization practices.
Serve as a trusted advisor for engineering, product, finance, and procurement teams on all matters related to FinOps (cloud financial management).
Stay updated with the latest AWS and Azure cloud technologies and industry best practices to continuously improve data engineering processes.
Qualifications
Minimum
Bachelor’s degree in computer science or engineering related field or equivalent work experience
Cloud certification such as Azure Solutions Architect Expert or AWS Certified Solutions Architect Associate or equivalent
10 years of IT experience including cloud FinOps or data engineering, with at least 2 years focused specifically on AI/ML workloads and LLM infrastructure
7 years of architecture experience or software development experience
3 years of cloud architecture
Desired Knowledge, Skills, and Abilities
FinOps Domain Expertise: Certified FinOps Professional with a proven track record of building chargeback and data quality frameworks from scratch.
AWS & Azure Expertise: Deep knowledge of AWS Cost & Usage Report (CUR), Azure Consumption Sheets, Amazon Bedrock, SageMaker, Azure OpenAI, and Azure AI Studio.
AI Tech Stack Literacy: Deep understanding of LLM application architecture, including tokenizers, vector databases, API gateways, and orchestration frameworks (LangChain, LlamaIndex).
Data Proficiency: Strong skills in SQL, Python, and cloud billing data pipelines.
Leadership & Influence: Ability to translate complex technical token mechanics into clear financial risk assessments for executive stakeholders.
Expert communication skills: ability to engage in technical conversations and influence stakeholders to prioritize FinOps related outcomes
Experience working with or leading an offshore team and interacting with technical teams as well as finance and accounting stakeholders.
Experience with FinOps tools and technologies, such as AWS Cost Explorer, Microsoft Cost Management, creation of custom FinOps dashboards
Familiarity with building automations and managing cloud infrastructure
Familiarity with the types of outcomes achievable through tools like Python, SQL, or cloud-native automation features is critical for guiding strategic automation opportunities within the team.
Experience with process improvement and defining & documenting Cloud policies and procedures to govern cost
Experience working with large data sets and tagging
Able to prepare and present technical material to various audiences from Application Developers to Executives.
Experience with forecasting AWS and Azure Gen-AI services
Experience with PowerBI and other dashboarding tools
Experience building dashboards and reports with FOCUS data
Familiarity with Hashicorp Terraform
Familiarity with ServiceNow
Professional and or Specialty AWS and Intermediate /Advanced Microsoft Certifications
Compensation
PG&E is providing the full salary/pay range for this position. The actual amount paid to an individual will be based on multiple factors, including, but not limited to, internal equity, specific skills, education, licenses or certifications, experience, market value, and geographic location. The range to reasonably expect will be around the minimum and the midpoint of the range. The final decision will be made on a case-by-case basis related to the factors above. This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.
Bay Area Min $155,000
Bay Area Mid: $210,000
Bay Area Max: $265,000
