Key Responsibilities- NLP & Generative AI Solutions: Develop and implement innovative solutions using Large Language Models (LLMs) for tasks such as classification, text analysis, and other Natural Language Processing (NLP) challenges.
- Retrieval-Augmented Generation (RAG): Design and build complete Retrieval-Augmented Generation (RAG) solutions to enhance the accuracy, relevance, and context-awareness of our AI-driven insights.
- Model Evaluation & Analysis: Conduct thorough evaluations and statistical analysis of LLMs and other machine learning models to ensure they meet high standards of performance, reliability, and business relevance.
- Insight Generation & Visualization: Transform ill-defined business problems into concrete analytical questions. Create quick and impactful visualizations and demos using tools like Streamlit to make complex findings accessible and engaging for stakeholders.
- Prompt Engineering: Skillfully craft and refine prompts to maximize the effectiveness and quality of LLM outputs, ensuring they deliver the best possible results for specific use cases.
- Cross-Functional Collaboration: Work closely with cross-functional teams and business stakeholders to understand project requirements, provide analytical support, and ensure smooth integration of models and APIs.
- Data Science Workflow: When necessary, develop and implement solutions using cloud-based platforms like Amazon SageMaker. Stay familiar with agentic workflows to improve the efficiency of data-driven systems.
- Stakeholder Communication: Communicate effectively with both internal and external clients or providers, ensuring a clear understanding of project goals, methodologies, and deliverables.
Required Qualifications- Previous experience in an applied data science or a related analytical role.
- Advanced proficiency in Python and its common data science libraries (e.g., pandas, scikit-learn, PyTorch/TensorFlow).
- Strong foundation in machine learning, statistics, and scientific methods.
- Hands-on experience with a major cloud platform (AWS, Azure, or GCP).
- Experience with containerization (Docker) for building and sharing analytical applications.
- Knowledge of API development and consumption (e.g., RESTful services).
- Demonstrated ability to learn and adapt to new technologies and methodologies quickly.
- Excellent problem-solving skills and the ability to work on ill-defined problems.
Preferred Qualifications

- Experience building and applying generative AI models.
- Deep knowledge of prompt engineering and LLM fine-tuning techniques.
- Experience with vector databases and implementing RAG solutions.
- Familiarity with MLOps tools and practices for model lifecycle management.
- Experience working in restricted or secure data environments.

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 and additional days-off along the year (+35 working days off in total).
- Attractive salary and compensation package.
- Hybrid model of working when possible, promoting the work-life balance (40% remote work).
- 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.