GeoAI Architect/GeoAI Lead

Join the growing Data & AI Unit as a GeoAI Architect / GeoAI Lead and take the lead in building cutting-edge geospatial and AI-driven solutions that connect technology, analytics, and real-world impact. This strategic role focuses on turning data into insights and tangible business value across industries where geospatial and Earth Observation (EO) data are central to digital transformation.

Project description

As part of strategic growth and commitment to innovation, this role aims to expand GeoAI capabilities by designing and delivering enterprise-grade geospatial architectures.

You will lead the creation of cloud-based GeoAI platforms, integrate satellite imagery and AI analytics into client systems, and help organizations unlock the business potential hidden in spatial data.

You’ll collaborate with multidisciplinary teams—from software engineers and data scientists to business stakeholders—to ensure that technical solutions are robust, scalable, and aligned with client objectives.

Tech stack

• AI / Machine Learning / Computer Vision: Python, ResNet, U-Net, YOLO, Transformers
• Geospatial & Earth Observation: Sentinel, Landsat, STAC, OGC, WMS, WMTS, TiTiler, GeoServer
• Cloud & Infrastructure: AWS, Azure or GCP, Docker, Kubernetes
• Data Processing & Orchestration: Dask, Airflow, Flyte, Argo, Prefect
• CI/CD & Development Tools: CI/CD pipelines (e.g., Jenkins, GitHub Actions, GitLab CI)

Requirements

• A degree in Business Administration, Computer Science, Engineering, or Geoinformatics / Earth Observation (or related field).
• Deep understanding of the geospatial domain, with strong knowledge of satellite and Earth Observation (EO) data sources (e.g., Sentinel, Landsat, commercial constellations).
• Previous experience in a Geo Data Engineer or similar role, with solid expertise in diverse geospatial data sources, standards, and technologies (STAC, OGC, WMS, WMTS, TiTiler, GeoServer).
• Proven hands-on experience delivering cloud-based geospatial platforms and orchestrating cloud-native data workflows (Kubernetes, Dask, Airflow, Flyte, Argo, Prefect).
• Strong proficiency in Python, cloud platforms (AWS, Azure, or GCP), and AI/ML techniques—including deep learning for computer vision and time series (ResNet, U-Net, YOLO, Transformers).
• Experience designing and implementing GeoAI architectures that combine AI/ML models with geospatial data pipelines.
• Strong understanding of AI/ML lifecycle management, from model training to deployment and monitoring.
• Excellent ability to structure complex issues, lead problem-solving efforts, and articulate clear strategies and recommendations.
• Proven track record in leading GeoAI projects or teams, managing stakeholder expectations, and maintaining strong client relationships.
• Innovative thinking and a proactive approach to identifying, framing, and solving technical and business challenges.
• Excellent communication and presentation skills in English, at both technical and executive levels.

Main responsibilities

• Design and lead GeoAI architectures and solutions for clients, integrating geospatial data, satellite imagery, and AI-driven analytics into enterprise systems.
• Build and deliver cloud-based geospatial platforms, prototypes, and PoCs that support commercial, research, and marketing initiatives.
• Contribute hands-on to client projects as a Software Engineer, developing and integrating scalable geospatial components, APIs, and data-processing pipelines.
• Participate directly in implementation, testing, and optimization to ensure the technical robustness, reliability, and performance of delivered solutions.
• Lead GeoAI projects and client engagements from pre-sales discussions and solution design through technical delivery and project execution.
• Support offer creation, define architectural direction, and align technical outcomes with client business objectives.
• Apply geospatial and Earth Observation expertise to identify and prioritize business challenges where AI and analytics can deliver measurable value.
• Collaborate with clients across industries to develop innovative, data-driven solutions.
• Bridge business and technical teams, ensuring effective communication among geo data engineers, data scientists, and client stakeholders.
• Translate business goals into actionable analytical tasks and ensure solutions are integrated into enterprise workflows.
• Ensure analytical insights are interpretable, actionable, and impactful, helping clients transform data outputs into concrete strategies and measurable results.
• Clearly communicate the benefits and business value of AI and geospatial solutions to foster adoption and long-term success.