Senior Project Manager, Delivery

Euclid Power is on a mission to accelerate the world’s transition to renewable energy. The Senior Project Manager, Delivery will drive the development and implementation of AI-assisted delivery capabilities, partnering cross-functionally to enhance services workflows and build comprehensive knowledge systems.

Responsibilities:

• Develop and implement a comprehensive, rigorous system for injecting Euclid’s knowledge library into AI-assisted feature context, including taxonomies, retrieval/selection logic, content governance, and quality controls
• Work cross-functionally with Engagement teams, Data, Product, and Engineering to ensure the knowledge system is continuously updated, accurate, and aligned with how Services teams actually execute work
• Partner with Engineering to implement and manage a prompt development, testing, and evaluation process, including prompt versioning, test suites, acceptance criteria, and performance tracking from the renewables domain perspective
• Design, build, and continuously improve AI-assisted diligence checklists and review frameworks, including defining required inputs, expected outputs, edge cases, and validation steps across Services and asset management use cases
• Identify, collect and maintain a catalogue of existing services workflows that can be enhanced through AI automation, develop improved processes, and operationalize those workflows either within Euclid’s platform or directly using ChatGPT and related tools
• Manage small working groups to develop, revise, and test prompts for multiple AI-assisted Euclid platform features, ensuring prompt outcomes are reliable, auditable, and aligned with customer expectations
• Translate renewable energy domain knowledge into prompt and system requirements, including definitions of key concepts, edge cases, error modes, and “what good looks like” for model outputs. Partner with Data Analytics and Services SMEs to curate, structure, and maintain Euclid’s internal knowledge library, leveraging existing data across Smartsheet, the Euclid Platform, and legacy documentation to ensure AI systems and services teams are working from consistent, trusted source material
• Build evaluation frameworks and rubrics for AI outputs (accuracy, completeness, interpretability, citation/traceability, tone, and user workflow alignment), and partner with teams to iterate quickly
• Define and track KPIs for AI-assisted delivery impact (time saved, error reduction, engagement team adoption, customer satisfaction impact), and communicate results and improvement priorities regularly
• Document and train internal teams on best practices for AI-assisted delivery, including prompt usage standards, workflow integration, and “human-in-the-loop” quality checks
• Act as an internal champion for practical AI adoption, balancing speed and experimentation with quality, risk management, and customer trust

Requirements:

• 4–6 years of experience in renewable energy, with strong familiarity with services workflows and customer-facing delivery (development, diligence, asset management, engineering, analytics, or similar)
• Demonstrated ability to run cross-functional initiatives, align stakeholders, and deliver outcomes — you're an operator, not just an idea generator
• Strong track record using AI to improve services-related workflows, and the ability to explain your approach to writing/revising/evaluating prompts and injecting relevant context to produce consistent, useful outputs
• Excellent written communication and documentation skills — you can turn messy inputs into clear, scalable standards
• Highly analytical and structured; comfortable designing evaluation frameworks and working with metrics. Comfort working in ambiguous, fast-evolving problem spaces where best practices are still being defined
• Comfortable collaborating with technical partners (engineering/data) even if you are not an engineer — you can translate between domain and product/technical requirements
• Bachelor's degree required (or equivalent professional experience)
• Experience working with knowledge systems (wikis, content governance, taxonomies, retrieval/search systems)
• Familiarity with experimentation design, QA processes, or model evaluation methodologies
• Prior experience contributing to AI product development (prompt libraries, agent workflows, evaluation sets, or adoption playbooks)