Senior AI Engineer Space

About Us

Space
42 (ADX: SPACE
42) is a UAE-based AI‑powered Space Tech company that integrates satellite communications, geospatial analytics and artificial intelligence capabilities to enlighten the Earth from space. Established in 2024 following the successful merger between Bayanat and Yahsat, Space
42’s global reach allows it to address the rapidly evolving needs of its customers in governments, enterprises, and communities.

Our vision is to pioneer beyond today for humanity to experience a better tomorrow. Space
42 challenges traditional approaches with advanced AI and cutting‑edge satellite technology, making space more accessible and redefining how data from space can be used on Earth. We aim to achieve this by connecting people to rewire potential, informing decisions to reimagine impact and enabling action to redefine tomorrow.

As a Senior AI Engineer – Geospatial Intelligence & Advanced AI, you will lead the design, implementation, and operationalization of production‑grade AI capabilities that turn multi‑source geospatial data into decision‑ready intelligence. You will work closely with Product Managers, Data Engineers, Software Developers, and domain experts to deliver scalable, reliable solutions across geospatial computer vision, graph/topology‑aware modeling, optimisation, and anomaly detection.

• Lead end‑to‑end delivery of ML/AI solutions from problem framing and prototyping to production deployment, monitoring, and continuous improvement.
• Provide technical direction on model selection, experimentation strategy, evaluation methodology, and architecture.
• Mentor and review work of AI Engineers; raise engineering standards through code reviews, design reviews, and reusable patterns.

• Build AI‑powered geospatial intelligence workflows that transform imagery and geospatial data into actionable outputs (alerts, change maps, object inventories, situational overlays).
• Partner with domain teams to define geospatial product requirements, acceptance criteria, and quality thresholds; translate them into model and pipeline requirements.
• Drive dataset strategy for GEOINT tasks (labeling guidelines, sampling, balancing, provenance, and ground‑truth validation).
• Ensure model outputs are map‑ready: georeferenced, interpretable, and compatible with cartographic and downstream GEOINT production pipelines.

• Design, train, tune, and validate Deep Learning and classical ML models for geospatial computer vision.
• Develop and evaluate Graph Neural Network (GNN) models and topological learning approaches.
• Apply graph‑based learning for transport networks, connectivity analysis, supply chains, and geospatial relationship modeling.
• Integrate advanced AI patterns where they measurably help, with clear evaluation, governance, and guardrails.
• Define and implement evaluation approaches for advanced AI components, ensuring outputs are reliable, attributable, and policy‑compliant.
• Build robust feature‑engineering pipelines and evaluation frameworks, including task‑specific metrics.

• Build and launch models in production using best practices in CI/CD, model versioning, experiment tracking, and automated testing.
• Define and manage SLAs/SLOs for model services.
• Implement monitoring for data quality, model drift, and operational health; drive incident triage and resolution.

• Collaborate with engineers, product managers, and analysts to understand business needs and data requirements.
• Partner with data infrastructure teams to improve dataset reliability, lineage, governance, and access patterns.
• Build and maintain production‑grade data pipelines that support training and inference.

• Develop clear documentation for models, pipelines, evaluation artifacts, and operational runbooks.
• Communicate trade‑offs and results to technical and non‑technical stakeholders.
• Collaborate in a multicultural and distributed environment.

Education

• Master’s degree in Computer Science, Statistics, Information Systems, or another…