Technical Manager - LLMs, RAG and ML at DNV

Oakland, California, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Sep, 26

Salary

0.0

Posted On

27 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Large Language Models, Retrieval-Augmented Generation, Vector Search, Prompt Engineering, AI Product Strategy, Machine Learning, Natural Language Processing, AI Evaluation, Solution Design, Cross-functional Leadership, Model Orchestration, Semantic Retrieval, Technical Roadmap Development, Responsible AI, Prototyping, Data Science

Industry

Public Safety

Description

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. About Energy Systems We help customers navigate the complex transition to a decarbonized and more sustainable energy future. We do this by assuring that energy systems work safely and effectively, using solutions that are increasingly digital. We also help industries and governments to navigate the many complex, interrelated transitions taking place globally and regionally, in the energy industry.

Responsibilities

Translate applied AI strategy into customer-ready capabilities, prototypes, and production-oriented solution designs for Energy Systems. Lead cross-functional efforts to develop and evaluate LLM-enabled workflows, focusing on RAG and responsible AI practices.