AI & Data Specialist (Open also To Protected Categories, Law 6899)

Overview

We are looking for an AI and Data Specialist to support the design development and deployment of AI-driven solutions. This role is primarily focused on AI while also contributing to the data analysis preparation and reporting activities required to build AI models.
The ideal candidate is a junior professional with strong technical foundations. They should be comfortable moving between experimentation implementation and communication of results to both technical and non-technical stakeholders.

Key Responsibilities

• AI solutions development:
• Design train test and deploy AI-based solutions to solve business problems
• Translate theoretical research and emerging AI techniques into practical and scalable applications. Evaluate and select appropriate approaches depending on the use case
• Support the business AI strategy by identifying relevant opportunities for automation prediction optimization and intelligent decision support. Contribute ideas on how AI can create value across processes products and services
• Support the full AI lifecycle including problem framing model development validation deployment monitoring and continuous improvement
• Contribute to the development of prototypes PoCs and production-ready AI tools
• Assess model performance and recommend improvements based on business objectives and technical constraints
• Support decision-making on model selection feature engineering optimization techniques and deployment options
• Help define best practices for responsible reliable and efficient use of AI within NAS
• Stay informed about new tools frameworks and trends in AI and data analytics and continuously assess their practical relevance for the business
• Data analysis:
• Help build and improve the data systems and pipelines needed to inform AI models
• Assist in the collection cleaning transformation interpretation and visualization of complex datasets
• Perform exploratory data analysis to identify patterns anomalies correlations and opportunities for model improvement
• Ensure data quality consistency and usability for AI and analytics purposes
• Collaborate with internal stakeholders to define data requirements and ensure availability of relevant information
• Contribute to the creation and maintenance of structured datasets for model training and evaluation
• Reporting and Visualization
• Produce clear and insightful dashboards/reports to communicate findings and support decision-making
• Present technical results in a way that is understandable and actionable for non-technical audiences.

Skills and qualifications

• Bachelors or masters degree in computer science or related field with foundational experience in AI model development
• Familiarity with Generative AI LLM-based applications prompt engineering pretrained model and finetuning retrieval-augmented generation systems AI agent and assistants
• Basic understanding of MLOps model deployment monitoring and maintenance
• Awareness of AI governance ethics bias explainability and data privacy principles
• Proficiency in Python and experience working with common AI/data framework and libraries (such us Pandas NumPy Scikit-learn Pytorch Tensorflow Keras HuggingFace LangChain Ollama etc.)
• Familiarity with machine learning workflows model evaluation protocols and performance metrics
• Basic knowledge of advanced AI paradigms (such as Online Learning Continual Learning Reinforcement Learning) for NLP Computer Vision and signal processing
• Experience with data visualization libraries and reporting tools (such as Power BI Matplotlib Seaborn)
• Strong SQL skills knowledge of relational DBs and confidence working with structured and unstructured datasets
• Familiarity with REST APIs and service integration for AI-driven applications.
• Problem-solving mindset with the ability to work independently on well-defined tasks and projects
• Attention to detail and a structured approach to experimentation and validation
• Familiarity with versioning control systems (Git/GitLab) containerization tools (Docker) and collaborative development practices

Key Competencies

• Strong analytical and critical thinking skills
• Curiosity and willingness to learn quickly
• Ability to bridge business needs and technical implementation
• Pragmatic approach to experimentation and delivery
• Ownership and accountability in managing tasks independently
• Good communication and stakeholder management skills
• Ability to prioritize and work across multiple initiatives