Technical Manager - LLMs, RAG and ML

Technical Manager - LLMs, RAG and ML is a strategic role within the Product organization, reporting to the Head of Section - AI Solutions. This role is responsible for translating applied AI strategy into customer-ready capabilities, prototypes, reusable technical patterns, evaluation methods, and production-oriented solution designs that support the Transformational Trust Initiative and broader digital platform enablement across Energy Systems.

This role is responsible for translating applied AI strategy into customer-ready capabilities, prototypes, reusable technical patterns, evaluation methods, and production-oriented solution designs that support the Transformational Trust Initiative and broader digital platform enablement across Energy Systems.

This role sits within Digital & Transformation and operates as a core product partner to the AI Solutions function, working across advisory regions, Renewable Certification, Digital & Data Solutions, Global Service Area Leads, engineering, data science, UX, and domain experts.

This position collaborates directly with the Head of Section - AI Solutions to identify high-impact AI opportunities, define product requirements, validate prototypes and proofs-of-concept, and support the delivery of scalable AI-driven workflows across Energy Systems.

This role is based at Oakland, CA, Irvine, CA office.

What You'll Do

AI Product Strategy & Roadmap Contribution

• Partner with the Head of Section - AI Solutions to translate the applied AI strategy into an executable technical roadmap, delivery plan, and prioritized set of AI product capabilities
• Contribute to roadmap definition, product vision, and technical strategy for LLM-enabled workflows across the Transformational Trust platform.
• Identify high-impact opportunities where AI, language models, retrieval systems, and automation can improve customer value, workflow efficiency, decision quality, and platform adoption.
• Help define technical standards, delivery patterns, and reusable components for AI-powered product development across advisory and digital platform workflows.
• Provide informed recommendations on model selection, retrieval architecture, evaluation approach, prompt strategy, orchestration patterns, and technical feasibility.

LLM, RAG and AI Solution Development

• Design, prototype, and guide delivery of AI-enabled capabilities using Large Language Models, generative AI foundations, Retrieval-Augmented Generation, vector search, and structured output techniques.
• Develop proof-of-concepts, reference implementations, and solution designs that engineering partners can implement, scale, and maintain.
• Design retrieval and indexing approaches for complex document, language, and workflow data, including semantic search, and context construction.
• Apply prompt engineering, tool/function calling, structured reasoning, and multi-step workflow patterns to support reliable AI-enabled product experiences.
• Support fine-tuning, LoRA, model adaptation, and evaluation approaches where needed to improve domain performance, task reliability, and user outcomes.

AI Evaluation, Quality and Responsible Use

• Establish practical evaluation methods for LLM-enabled workflows, including accuracy, relevance, reliability, retrieval quality, user experience, and task completion metrics.
• Develop benchmark approaches, test datasets, evaluation rubrics, and monitoring methods to support reliable AI performance over time.
• Partner with the Head of Section, data science, engineering, and governance stakeholders to support responsible AI practices, including transparency, risk identification, data considerations, and appropriate human oversight.
• Define quality standards for prototypes, AI components, prompt patterns, retrieval workflows, and production handoffs.
• Track and communicate AI product performance against KPIs tied to customer value, workflow efficiency, model quality, and responsible AI outcomes.

Cross Functional Technical Leadership

• Lead cross-functional AI delivery efforts across product, engineering, data science, UX, domain experts, and customer-facing teams.
• Serve as a technical translator between business needs, user workflows, AI methods, and engineering implementation.
• Provide technical guidance to teams working on LLM applications, retrieval systems, chatbot or agentic workflows, data pipelines, and AI-enabled decision support.
• Support rapid prototyping and iteration while maintaining a disciplined approach to quality, scalability, and responsible deployment.
• Coach emerging AI and product talent as the AI Solutions function grows, helping build a culture of experimentation, rigor, inclusion, and continuous improvement.