AI Product Manager (Renewable Energy)

Cypress Creek Renewables•Durham, NC

1d•Hybrid

About The Position

Cypress Creek Renewables is powering a sustainable future, one project at a time. We develop, finance, own and operate utility-scale and distributed solar and storage projects across the country. Fostering a diverse group of innovative thinkers from all backgrounds, Cypress people are drawn to work in a purpose-driven organization. We hope you will join us. CCRenew is seeking an experienced AI Product Manager / Engineer to drive the development and deployment of artificial intelligence and machine learning solutions across our solar project development lifecycle. This is a high-impact, cross-functional role sitting at the intersection of renewable energy domain expertise and advanced technology. Reporting directly to the Chief Technology Officer, you will own the product roadmap for AI-powered tools that streamline site selection, EPC procurement and management, interconnection analysis, and construction oversight. The ideal candidate brings a rare combination of hands-on experience in solar development—including navigating EPC contracts, interconnection queues, and greenfield site assessment—and proven ability to translate complex business problems into scalable AI/ML products. You will partner closely with project development, engineering, and operations teams to identify high-value automation and intelligence opportunities, then lead end-to-end delivery.

Requirements

7+ years of direct experience in solar energy, with significant time spent in the development phase of utility-scale projects.
Deep familiarity with the full solar development lifecycle: site control, permitting, interconnection, EPC procurement, construction, and commercial operations.
Hands-on experience navigating interconnection processes with one or more ISOs/RTOs (MISO, PJM, CAISO, SPP, ERCOT, NYISO, or Southeast utilities).
Working knowledge of EPC contract structures, lump-sum vs. cost-plus arrangements, LD provisions, and construction risk allocation.
Understanding of solar site selection criteria including GHI/DNI analysis, land use constraints, transmission access, and environmental review processes.
Proven track record delivering AI or ML products from concept through production deployment.
Proficiency in Python and familiarity with core ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost, or similar).
Experience with geospatial data analysis (GeoPandas, QGIS, ArcGIS, Google Earth Engine, or equivalent).
Familiarity with LLM application development, prompt engineering, and RAG architectures for document intelligence use cases.
Comfort working with cloud platforms (AWS, Azure, or GCP) and modern data infrastructure (Snowflake, dbt, Spark, Airflow).
Demonstrated ability to own a product roadmap and drive delivery in a fast-paced, cross-functional environment.
Strong written and verbal communication skills; ability to present complex technical concepts to non-technical stakeholders, including C-suite.
Experience working in or alongside development, engineering, or project finance teams at an IPP, developer, or EPC firm.
Embrace and live by the mission and values of Cypress Creek Renewables.

Nice To Haves

Master’s degree or PhD in Computer Science, Data Science, Electrical Engineering, Environmental Science, or a related field.
Prior experience in a product manager or technical product lead role at a renewable energy company, energy tech startup, or utility.
Familiarity with FERC regulations, NERC standards, and state-level renewable energy policy frameworks.
Experience with computer vision or remote sensing applications (e.g., aerial/satellite image analysis for construction progress or land assessment).
Knowledge of energy storage (BESS) co-location development and its impact on interconnection and site planning.
Background in power flow modeling or transmission planning tools (PSS/E, PowerWorld, PSCAD).
Experience with project management platforms such as Procore, Oracle Primavera, or similar construction management tools.

Responsibilities

Define and own the AI product roadmap aligned with CCRenew’s solar development pipeline strategy and CTO vision.
Identify opportunities to apply machine learning, computer vision, geospatial AI, and predictive analytics across the development lifecycle.
Translate domain pain points in site selection, EPC management, interconnects, and construction into structured, prioritized product requirements.
Build and maintain stakeholder alignment across development, engineering, finance, and legal teams.
Lead development of AI-driven site screening and scoring tools that evaluate land availability, solar irradiance, transmission proximity, environmental constraints, and permitting risk.
Integrate geospatial data sources (LiDAR, satellite imagery, GIS layers) into automated site analysis workflows.
Build predictive models for site feasibility scoring that reduce manual assessment time and improve go/no-go decision quality.
Develop AI tools to model and forecast interconnection queue positions, study timelines, and upgrade cost scenarios across MISO, PJM, CAISO, ERCOT, and other ISOs/RTOs.
Build automated monitoring systems for regulatory queue changes, FERC Order compliance, and interconnection agreement milestones.
Apply NLP and document intelligence to process study results, interconnection agreements, and utility correspondence at scale.
Design AI-powered tools for EPC bid analysis, contract risk scoring, and subcontractor performance benchmarking.
Develop cost estimation and variance prediction models informed by equipment pricing trends, labor markets, and project-specific variables.
Automate review of EPC contracts, change orders, and scope documents using large language model (LLM) pipelines.
Build AI solutions for real-time construction progress tracking using drone imagery, IoT sensor data, and project management integrations.
Develop schedule risk and delay prediction models that surface issues early and recommend mitigation actions.
Create automated reporting and alerting systems that consolidate field data, RFI logs, inspection records, and milestone tracking.
Architect and, where required, directly engineer ML pipelines, data ingestion systems, and model deployment infrastructure.
Partner with data engineering and software engineering to ensure production-grade reliability, scalability, and security of AI systems.
Establish model monitoring, drift detection, and retraining workflows for deployed models.
Evaluate and integrate third-party AI tools, APIs, and vendor platforms relevant to the solar development stack.