Senior AI Engineer, Space42 (G42), Group 42 (G42)

Job Description

The Senior AI Engineer at Space42 designs and develops advanced AI solutions that convert geospatial and satellite data into actionable intelligence which they deploy to operational environments. The position requires engineering work while also providing technical direction and working with different departments to create AI systems that can operate at full scale in production environments which use geospatial analytics and computer vision and graph-based modeling technologies.

Job ID: 2733

Date Posted: NA

Expiration Date: NA

Qualification: Master’s Degree (PhD preferred)

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Main Duties

• Lead end-to-end AI/ML solution development from prototyping to production deployment and monitoring
• Design and implement geospatial intelligence workflows using satellite and remote sensing data.
• Develop deep learning and graph-based models including Graph Neural Networks for spatial analysis.
• Build scalable ML pipelines, data processing systems, and production-grade MLOps frameworks.
• Collaborate with cross-functional teams to define product requirements and deliver AI-driven insights.
• Ensure model reliability through monitoring, evaluation frameworks, and continuous improvement.
• Mentor engineers, conduct code reviews, and drive best practices in AI system design.

Essential Qualifications

• A Master’s degree in Computer Science Statistics or related quantitative field requires PhD as the preferred qualification.
• Five years of work experience in software engineering data engineering and ML engineering positions.
• Three years of practical experience in developing and implementing AI/ML models for real-world applications.
• Demonstrates advanced skills in Python programming together with expertise in ML frameworks including PyTorch TensorFlow and scikit-learn.
• Expertise in three distinct areas including geospatial intelligence, computer vision and satellite imagery analysis.
• Demonstrates complete knowledge about Graph Neural Networks together with expertise in relational data modeling.
• Experience in developing machine learning systems together with building data pipelines and processing extensive datasets.

Preferred Qualifications

• Experience in using geospatial tools which include GDAL, GeoPandas, PostGIS and cloud-optimized formats.
• Knowledge about AWS and GCP cloud platforms together with their scalable infrastructure systems.
• MLOps tools which include MLflow and Kubeflow and Airflow and DVC.
• Docker and Kubernetes and distributed systems environments.
• Multimodal AI systems together with LLMs and GEOINT workflows.
• Possesses experience in cartography and photogrammetry and satellite data processing pipelines.
• Communicate effectively while collaborating with international teams that work across different functions.