AI Engineer

Role

Purpose

To Implement the architectural design, algorithmic engineering, and deployment of Artificial Intelligence and Machine Learning systems to support R&D initiatives and solutions. Harness operational and maintenance data to design and develop operationally valid, production-grade AI solutions by bridging the gap between theoretical data science prototypes and live operational systems. Ensure compliance with aviation-specific regulatory and safety-critical constraints throughout concept development, within defined KPIs, approved budgets, and established policies and procedures.

Key

Accountability

Areas

Key

Activities

AI Infrastructure & MLOps

• Design, develop, and maintain highly robust MLOps pipelines to autonomously ingest, process, and validate large-scale structured and unstructured data streams for continuous model training and real-time inference.
• Develop, fine-tune, and orchestrate advanced Machine Learning and Deep Learning architectures, including Natural Language Processing (NLP), computer vision, and Generative AI/Agentic workflows.
• Design, develop, and implement predictive and prescriptive analytics models, ensuring high availability, determinism, and strict fault tolerance.
• Gather, organize, and analyze industry research and trends in applied AI methodologies and computational infrastructure to provide competitive intelligence that informs R&D strategies.
• Utilize infrastructure-as-code, containerization (e.g., Docker, Kubernetes), and enterprise cloud-based ML platforms (e.g., Vertex AI, MLflow, SageMaker) to build reproducible AI environments and facilitate seamless CI/CD model deployment.
• Conduct the exploration of emerging technologies (e.g., deep learning, graph analytics, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and the Model Context Protocol) and assess their applicability to ATM R&D use cases.
• Optimize computational performance for large-scale models, utilizing advanced hardware acceleration and techniques such as model quantization and weight pruning to drastically reduce latency and memory footprint, ensuring scalability and real-time readiness.

R&D Prototyping & Productization

• Apply advanced algorithm engineering practices to assess and validate Proof of Concept (PoC) initiatives, transitioning them efficiently from isolated research sandbox environments into viable, secure software solutions.
• Develop innovative AI solutions that support R&D concepts progressing through different stages of the technology readiness levels (TRLs), acting as the critical engineering bridge between scientific theory and enterprise software implementation.
• Collaborate with the Knowledge & Insights team to integrate industry research and trends into algorithmic models, ensuring alignment with future R&D priorities.
• Drive prototype development by translating R&D concepts into demonstrable end-to-end software solutions, influencing key go/no-go innovation decisions.
• Contribute to cross-disciplinary projects that bring together software engineering, operations, and data science expertise, ensuring alignment with strategic R&D roadmaps and continuous integration/continuous deployment (CI/CD) pipelines.

Intelligent Systems & API Integration

• Monitor and improve deployed AI models and analytics tools for real-time or near-real-time applications in air traffic management, establishing automated observability and alerting pipelines to detect performance degradation, schema drift, and data drift.
• Provide programmatic algorithmic frameworks that autonomously optimize airspace usage, reduce delays, and improve safety outcomes for aviation operations.
• Collaborate with engineers, domain experts, and stakeholders to define problems and deliver innovative AI-powered automation software solutions.
• Translate complex algorithmic capabilities into actionable insights that are understandable for technical and non-technical audiences alike, fostering data-driven decision-making and ensuring smooth, compliant Human-AI Teaming (HAT) protocols are established.
• Establish highly scalable REST/gRPC APIs, microservices, and decision-support backend architectures to interface with existing legacy engineering systems.

Safety-Critical AI Engineering & Assurance

• Ensure all AI and machine learning activities comply with regulatory standards and ethical considerations, maintaining integrity and transparency in alignment with international benchmarks (e.g. EASA Trustworthy AI principles).
• Incorporate safety-critical constraints into AI software architectures to meet aviation industry requirements and ensure operational feasibility under severely constrained environments.
• Regularly audit data models and algorithmic processes to identify risks, mitigate algorithmic bias, guarantee corner-case robustness, and ensure highly deterministic system behavior.
• Support the secure management of sensitive and critical data, ensuring alignment with THE ANSP’s data governance and cybersecurity policies to actively harden models against adversarial manipulation and data poisoning attacks.
• Lead the implementation of advanced AI governance frameworks (including rigorous model versioning, mathematical explainability mechanisms, and learning assurance protocols).
• comply with relevant regulatory bodies and industry forums (e.g., EASA, EUROCAE WG-114, SAE G34) to ensure AI development practices and safety cases are aligned with evolving international aviation certification standards.

Policies, Processes and Procedures

• Conduct day-to-day activities while ensuring compliance to policies and procedures.
• Contribute to the identification of opportunities for continuous improvement of systems and software architectures, taking into account leading practices, changes in business environment, cost reduction and productivity improvement.

Knowledge

and

Experience

• No prior years of experience is required

Education

and

Certifications

• Bachelor’s degreein Artificial Intelligence, software engineering, computer science or equivalent is required.
• Preferred Certifications: machine learning, AI, or data analytics or Enterprise Cloud Architecture (e.g., Google Professional Machine Learning Engineer, AWS Certified Machine Learning)