Agentic AI Engineer, Expert

Pacific Gas And Electric Company•Oakland, CA

5d•$140,000 - $238,000•Hybrid

About The Position

The AI and Cloud Engineering Team is responsible for defining PG&E's enterprise AI and Cloud platform technical roadmap and strategic architecture standards, Agentic AI patterns, MCP integrations, AI Gateway services, knowledge platforms, and AI Foundation capabilities. The team provides design and engineering leadership for enterprise AI adoption. Builds AI foundational capabilities to support PG&E high impact use cases. Ensure AI solutions are scalable, secure, governed, and aligned with business and technology objectives. The Agentic AI Engineer, Expert is responsible for leading the architecture and design of PG&E's AI ecosystem. This role defines architecture standards, reference patterns, and strategic technical direction for Agentic AI platforms, AI Foundation services, MCP integrations, AI Gateways, retrieval platforms, knowledge systems, and enterprise AI solutions. The position serves as the principal architecture authority for AI platforms and emerging AI technologies across PG&E. 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 $140,000 - $189,000. 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 Minimum: $ 140,000 Bay Area Maximum: $ 238,000.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Technology, Artificial Intelligence, or equivalent work experience in computer science, information technology, business administration, engineering, or other relevant field required
5 years of experience in Cloud Architecture, Solution Architecture, Platform Architecture, or Enterprise Architecture
AWS Associate Certification, or equivalent Cloud platform certification

Nice To Haves

Experience with Agentic AI platforms, AI Gateways, MCP, Copilot Studio, Azure AI Foundry, AWS Bedrock, and enterprise AI services
5+ years of software development experience
3+ years of experience architecting AI, ML, Generative AI, or Agentic AI solutions
Experience architecting cloud-native platforms within AWS and Azure environments
Experience designing enterprise integration architectures and API-based platforms
Experience leading architecture and design review processes
Strong executive communication and stakeholder management skills
Experience designing RAG, semantic search, vector database, and knowledge graph solutions
Experience defining enterprise AI operating models, governance frameworks, and Responsible AI controls
Experience with developer platforms, DevSecOps, AI observability, and GenAIOps
Experience in utility, critical infrastructure, or highly regulated environments
Experience developing enterprise-wide AI platform roadmaps and transformation strategies

Responsibilities

Work closely with AI Product Owners to define AI platform architecture, standards, reference architectures, and roadmaps.
Establish enterprise AI foundational architecture and shared services technical strategy.
Develop reusable architecture patterns for AI applications and services.
Align AI platform investments with business strategy and enterprise priorities.
Build prototypes for new Agentic patterns and explore open sourced solutions and frameworks.
Architect multi-agent systems, autonomous workflows, and intelligent orchestration capabilities.
Establish Agent-to-Agent (A2A) communication and Human-in-the-Loop (HITL) design standards.
Design reusable Agentic AI architecture patterns and implementation frameworks.
Guide enterprise adoption of Agentic AI solutions.
Establish architecture standards for AI Gateways and enterprise AI traffic management.
Define MCP architecture and enterprise connectivity patterns.
Architect secure integrations between AI platforms and enterprise systems.
Develop reusable AI integration frameworks and reference solutions.
Architect enterprise RAG, semantic search, vector database, and knowledge graph platforms.
Define enterprise context management and retrieval architecture patterns.
Establish reference architectures for enterprise knowledge services.
Provide architectural leadership for AI-powered knowledge enablement solutions.
Build evals for RAG and KnowledgeGraph systems and use in production for drift detection.
Work closely with Cybersecurity teams to define AI security guardrails and platform architecture controls.
Establish AI governance and Responsible AI architecture standards.
Review solution architectures for security, privacy, compliance, and risk requirements.
Partner with Cybersecurity and Enterprise Architecture teams to govern enterprise AI platforms.
Design enterprise AI solutions that embed FinOps principles by default, optimizing model selection, infrastructure utilization, cost transparency, governance, and scalability to maximize business value while controlling operational spend.
Lead enterprise AI architecture reviews and strategic design sessions.
Advise business and technology leaders on enabling AI use cases.
Evaluate emerging AI technologies and platform capabilities.
Mentor architects and engineering teams across the organization.