AI Engineer

About the Role

Saudi Air Navigation Services is seeking an AI Engineer to join their team in Jeddah. This full-time role focuses on implementing architectural design, algorithmic engineering, and the deployment of Artificial Intelligence and Machine Learning systems. The position requires 0-1 years of experience and involves supporting R&D initiatives and solutions within the aviation sector.

Role Purpose and Context

The primary purpose of this role is to harness operational and maintenance data to design and develop operationally valid, production-grade AI solutions. This involves bridging the gap between theoretical data science prototypes and live operational systems. The AI Engineer will ensure compliance with aviation-specific regulatory and safety-critical constraints throughout concept development, adhering to defined KPIs, approved budgets, and established policies and procedures.

Key Responsibilities

• Design, develop, and maintain robust MLOps pipelines for data ingestion, processing, and validation, supporting continuous model training and real-time inference.
• Develop, fine-tune, and orchestrate advanced Machine Learning and Deep Learning architectures, including NLP, computer vision, and Generative AI/Agentic workflows.
• Implement predictive and prescriptive analytics models, ensuring high availability, determinism, and fault tolerance.
• Analyze industry research and trends in applied AI methodologies and computational infrastructure to inform R&D strategies.
• Utilize infrastructure-as-code, containerization (*, Docker, Kubernetes), and enterprise cloud-based ML platforms (*, Vertex AI, MLflow, SageMaker) to build reproducible AI environments and facilitate CI/CD model deployment.
• Explore emerging technologies such as deep learning, graph analytics, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG), assessing their applicability to ATM R&D use cases.
• Optimize computational performance for large-scale models using hardware acceleration and techniques like model quantization and weight pruning.
• Apply advanced algorithm engineering practices to validate Proof of Concept (PoC) initiatives and transition them into secure software solutions.
• Develop innovative AI solutions that support R&D concepts through various technology readiness levels (TRLs), bridging scientific theory and enterprise software implementation.
• Collaborate with the Knowledge & Insights team to integrate industry research into algorithmic models, aligning with future R&D priorities.
• Drive prototype development by translating R&D concepts into demonstrable end-to-end software solutions, influencing innovation decisions.
• Contribute to cross-disciplinary projects involving software engineering, operations, and data science, ensuring alignment with strategic R&D roadmaps and CI/CD pipelines.
• Monitor and improve deployed AI models and analytics tools for real-time applications in air traffic management, establishing automated observability and alerting pipelines.
• Provide programmatic algorithmic frameworks that optimize airspace usage, reduce delays, and improve safety outcomes for aviation operations.
• Collaborate with engineers, domain experts, and stakeholders to define problems and deliver AI-powered automation software solutions.
• Translate complex algorithmic capabilities into actionable insights for technical and non-technical audiences, fostering data-driven decision-making and compliant Human-AI Teaming (HAT) protocols.
• Establish scalable REST/gRPC APIs, microservices, and decision-support backend architectures to interface with existing legacy engineering systems.
• Ensure all AI and machine learning activities comply with regulatory standards and ethical considerations, maintaining integrity and transparency in alignment with international benchmarks (*, EASA Trustworthy AI principles).
• Incorporate safety-critical constraints into AI software architectures to meet aviation industry requirements and ensure operational feasibility in constrained environments.
• Regularly audit data models and algorithmic processes to identify risks, mitigate algorithmic bias, guarantee corner-case robustness, and ensure deterministic system behavior.
• Support the secure management of sensitive and critical data, aligning with data governance and cybersecurity policies to harden models against adversarial manipulation and data poisoning attacks.
• Lead the implementation of advanced AI governance frameworks, including model versioning, mathematical explainability mechanisms, and learning assurance protocols.
• Comply with relevant regulatory bodies and industry forums (*, EASA, EUROCAE WG-114, SAE G34) to ensure AI development practices and safety cases align with evolving international aviation certification standards.
• Conduct day-to-day activities in compliance with policies and procedures.
• Contribute to identifying opportunities for continuous improvement of systems and software architectures, considering leading practices, business environment changes, cost reduction, and productivity improvement.

Experience and Qualifications

• 0-1 years of relevant experience.