Airbus is a global pioneer in the aerospace industry, specializing in the design, manufacture, and delivery of commercial aircraft, military transports, helicopters, and space systems. As one of the world's largest aerospace companies, it employs over 130,000 people globally and operates as a cornerstone of European industrial collaboration and technological innovation. Spain plays a vital role in Airbus's global operations, hosting major production and engineering centers in Getafe, Seville, and Cádiz. The company is a major employer for international talent in Spain, offering a multicultural work environment where English is the primary language for technical and management roles, alongside comprehensive relocation support for specialized engineering positions.

Airbus was established in 1970 as a European consortium to challenge the dominance of American aerospace manufacturers. It grew through the integration of major national aerospace companies from France, Germany, the UK, and Spain, including the merger with Spain's CASA in 1999.

AI Systems Engineer (LLM & RAG Optimization)

Airbus Spain

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Job Description:

Airbus Defence & Space is looking for an LLM Inference Engineer to optimize the efficiency and scalability of Generative AI systems.

The selected candidate will join the Architecture & Integration team at CLAEX, in Torrejón de Ardoz Air Base, with the goal of optimizing the deployment and performance of Large Language Models (LLMs).

Their mission will be to ensure that AI systems are fast, accurate, and capable of serving multiple simultaneous users, laying the groundwork for future evolution toward training and fine-tuning open models.

We are looking for an ML Engineer with a hybrid focus on applied Artificial Intelligence and high-performance system optimization. We are not just looking for someone who uses models, but for an expert capable of "squeezing the maximum potential" out of hardware (GPUs) to deliver high-quality responses with the lowest possible latency.

Key Responsibilities

Model Serving Optimization: Design and manage the deployment of Large Language Models (LLMs), optimizing memory usage and responsiveness for high-demand environments.
RAG (Retrieval-Augmented Generation) System Architecture: Design and refine workflows that enable AI to accurately query private information, managing everything from document ingestion to intelligent data retrieval.
Multi-GPU High-Performance Management: Configure and optimize workload distribution across multiple GPUs to maximize processing speed and user concurrency.
Quality and Accuracy Assurance: Implement evaluation methodologies to reduce hallucinations, improve response relevance, and ensure the AI is a reliable tool for the end user.
Data Pipeline Development: Create efficient processes for transforming complex documents (PDF, OCR, etc.) into formats optimized for AI learning and querying.
Service and API Exposure: Develop standardized and secure communication interfaces to integrate AI capabilities with other applications and user platforms.
Technological Evolution: Research and prepare the infrastructure for future phases involving training, quantization, and model adaptation to specific needs.

Requirements

Solid experience (3+ years) in Machine Learning Engineering, Natural Language Processing (NLP), or Applied AI.
Degree in Computer, Telecomunications, Maths or Software Engineering.
Hands-on experience in the deployment and productionization of Large Language Models (LLMs).
Advanced proficiency in Python.
Proven experience building information retrieval architectures (RAG systems) and vector databases.
Demonstrated ability to optimize computing resources (GPU/CPU/Memory) to enhance AI system performance.
B2 level in English.

Preferred Qualifications

Military Avionics and embedded/Real Time Software knowledge is desirable. This is useful since the LLM training and inference is targeted at supporting Military Avionics development and most of the task would be related to military Avionics.
Experience working with large-scale models.
Knowledge of model optimization techniques (quantization, model weight reduction).
Experience managing very large information contexts (long-context windows).
Knowledge of containerized environments (Kubernetes) and high-security (air-gapped) environments.
Previous experience in fine-tuning or training language models.

WHICH BENEFITS WILL YOU HAVE AS AIRBUS EMPLOYEE?

At Airbus we are focused on our employees and their welfare. Take a look at some of our social benefits:

Vacation days plus additional days-off along the year.
Attractive salary.
Hybrid model of working when possible, promoting the work-life balance.
Collective transport service in some sites.
Benefits such as health insurance, employee stock options, retirement plan, or study grants.
On-site facilities (among others): free canteen, kindergarten, medical office.
Possibility to collaborate in different social and corporate social responsibility initiatives.
Excellent upskilling opportunities and great development prospects in a multicultural environment.
Special rates in products & benefits.

This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company's success, reputation and sustainable growth.

Company:

Airbus Defence and Space SAU

Employment Type:

Permanent

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Experience Level:

Professional

Job Family:

Software Engineering

By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus.
Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.

Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to emsom@airbus.com.