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 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.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.Health insurance, employee stock options, retirement plan, or study grants.On‑site facilities (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.
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Company : Airbus Defence and Space SAU
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