GeoSpatial Engineer MAP will design implement and optimize advanced geospatial solutions powered by open source large language models Google Document AI and generative AI. The role focuses on building scalable mapping workflows automating document driven spatial analysis and enabling data driven decisions for global business functions in a hybrid work setup with day shift and no travel requirements.
Responsibilities
- Design robust geospatial data pipelines that integrate mapping datasets with open source large language models to enable intelligent spatial analysis and automated insight generation across complex business domains.
- Develop and maintain scalable geospatial applications that combine vector and raster processing with generative AI techniques to support advanced map visualization and scenario modeling for enterprise users.
- Implement end to end workflows that use Google Document AI to extract structured geospatial information from unstructured documents and seamlessly fuse this data into existing spatial repositories for accurate downstream analytics.
- Optimize query performance on large geospatial data stores by tuning spatial indexing coordinate transformations and model inference flows to ensure responsive map experiences for internal and external stakeholders.
- Collaborate with cross functional teams to translate business requirements into geospatial and AI driven solutions that improve operational efficiency risk assessment and strategic planning for diverse regions.
- Create and refine prompt engineering strategies for open source large language models to generate high quality geospatial narratives anomaly descriptions and decision support summaries tailored to domain specific needs.
- Build reusable components that orchestrate data ingestion geocoding feature engineering and model deployment to streamline the development of new mapping products and reduce time to market.
- Monitor production systems for data quality model drift and geospatial accuracy and apply corrective actions that preserve reliability regulatory compliance and trust in generated outputs.
- Document solution architectures data lineage and mapping rules in explicit technical artifacts that support auditability onboarding and continuous improvement in a hybrid work environment.
- Guide adoption of responsible AI practices in geospatial solutions by enforcing privacy aware data handling transparent model behavior and ethical impact assessment for communities represented in the data.
- Coordinate with platform and infrastructure teams to ensure that geospatial services AI models and document processing pipelines are secure scalable and aligned with enterprise standards for day shift operations.
- Engage in continuous learning and experimentation with emerging geospatial libraries and generative AI frameworks to recommend enhancements that expand mapping capabilities and societal impact.
- Contribute to knowledge sharing through internal forums and technical reviews that spread best practices in geospatial engineering open source AI usage and document intelligence across the organization.
Qualifications
- Possess six to ten years of experience in geospatial engineering or closely related fields with a strong background in designing and implementing mapping solutions for complex business environments.
- Demonstrate hands on expertise in open source large language models including configuration fine tuning and prompt design for geospatial and document centric workflows.
- Show proven capability in using Google Document AI or equivalent document intelligence platforms to extract validate and operationalize structured spatial data from diverse document sources.
- Exhibit solid understanding of generative AI techniques and their application to narrative generation pattern discovery and scenario simulation within geospatial contexts.
- Apply strong proficiency in geospatial concepts such as coordinate reference systems spatial joins topology and geocoding supported by practical experience with common geospatial tools and libraries.
- Utilize advanced skills in programming and scripting languages relevant to data engineering and AI implementation to automate pipelines and integrate models with mapping applications.
- Demonstrate experience working in hybrid delivery models with effective communication documentation and remote collaboration practices that support day shift operations and no travel expectations.