Renewable Energy Systems Engineer
7 days ago
Cleantech Industry Resources Overview
We are a leading provider of turnkey development as a service, accelerating United States solar, battery storage, and EV projects. Our sophisticated software suite supports projects from land origination to commercial operation.
About the Role
We are seeking an experienced AI/ML Engineer to join our team and build machine learning models for GIS-based spatial analysis and data-driven decision-making. This role involves working on geospatial AI models, data pipelines, and Retrieval-Augmented Generation (RAG)-based applications for zoning, county sentiment analysis, and regulatory insights.
Key Responsibilities
- Develop and deploy ML models for geospatial data processing, forecasting, and automated GIS insights.
- Work with large-scale geospatial datasets, including satellite imagery, shapefiles, raster/vector data.
- Create AI models for land classification, feature detection, and geospatial pattern analysis.
- Optimize spatial data pipelines and build predictive models for environmental and energy sector applications.
- Develop RAG-based AI applications to extract insights from zoning, permitting, and regulatory documents.
- Build LLM-based applications for zoning law interpretation, county sentiment analysis, and compliance predictions.
- Implement document retrieval and summarization techniques for legal, policy, and energy development reports.
Required Skills and Qualifications
- Masters in Computer Science, Data Science, Machine Learning, Geostatistics, or related fields.
- Programming skills in Python, SQL, and experience with TensorFlow, PyTorch, scikit-learn, pandas, NumPy.
- Machine learning and AI skills, including deep learning, NLP, retrieval-based AI, geospatial AI, predictive modeling.
- GIS and spatial data processing skills, including PostGIS, GDAL, GeoPandas, QGIS, Google Earth Engine.
- LLM and RAG development skills, including fine-tuning LLMs, retrieval models, vector databases (FAISS, Weaviate).
- Cloud and MLOps skills, including AWS/GCP/Azure, Docker, Kubernetes, MLflow, FastAPI.
- Big data processing skills, including large-scale data mining, data annotation, and knowledge graph techniques.
- Database and storage skills, including PostgreSQL, NoSQL, vector databases, cloud storage solutions.
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