AI Engineer

About the Role

The primary objective of this role is to implement architectural designs, algorithmic engineering, and the deployment of Artificial Intelligence (AI) and Machine Learning (ML) systems to support Research and Development (R&D) initiatives. This position focuses on transforming theoretical data science prototypes into operational systems that comply with aviation-specific regulations and safety-critical constraints. You will work within defined KPIs, approved budgets, and established policies and procedures to deliver robust AI solutions.

Responsibilities

Key Accountability Areas

AI Infrastructure & MLOps

• Design, develop, and maintain highly robust MLOps pipelines for ingesting, processing, and validating large-scale structured and unstructured data streams, enabling 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.
• Create and implement predictive and prescriptive analytics models, ensuring high availability, determinism, and strict fault tolerance.
• Analyze industry research and trends in applied AI methodologies 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 continuous integration/continuous deployment (CI/CD) model deployment.
• Explore emerging technologies (e.g., deep learning, graph analytics, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG)) and assess their applicability to ATM R&D use cases.
• Optimize computational performance for large-scale models, utilizing advanced hardware acceleration, model quantization, and weight pruning to reduce latency and memory footprint while 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 environments into viable, secure software solutions.
• Develop innovative AI solutions that support R&D concepts progressing through various stages of technology readiness levels (TRLs), serving as the 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 strategy alignment with R&D priorities.
• Drive prototype development by translating R&D concepts into demonstrable software solutions, influencing key go/no-go innovation decisions.
• Contribute to cross-disciplinary projects that integrate software engineering, operations, and data science expertise while ensuring alignment with R&D roadmaps and CI/CD pipelines.

Intelligent Systems & API Integration

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

Safety-Critical AI Engineering & Assurance

• Ensure AI and ML activities adhere to 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 architectures to meet aviation industry requirements, ensuring operational feasibility under constrained environments.
• Conduct regular audits of data models and algorithmic processes to identify risks, mitigate bias, and ensure deterministic system behavior.
• Support secure management of sensitive data in compliance with governance and cybersecurity policies, ensuring models are safeguarded against adversarial attacks and data poisoning.
• Lead the implementation of advanced AI governance frameworks, including model versioning, explainability, and learning assurance protocols.
• Ensure compliance with relevant regulatory bodies and industry forums (e.g., EASA, EUROCAE WG-114) to align AI development practices with international aviation certification standards.

Policies, Processes and Procedures

• Conduct daily activities while ensuring adherence to company policies and procedures.
• Contribute to opportunities for continuous improvement of systems and software architectures, considering best practices, cost reduction, and productivity enhancements.

Qualifications

Knowledge and Experience

• No prior years of experience required.

Education and Certifications

• Bachelor’s degree in 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).