AI Engineer

RolePurpose

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.

KeyAccountabilityAreas

KeyActivities

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.

KnowledgeandExperience

• No prior years of experience is required

Educationand Certifications

• Bachelor’sdegreein 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)