AI specialist

Area of responsibility:

• Strategic AI Solutions:
• proposing innovative solutions that align with business objectives.
• Developing and maintaining AI systems and applications, ensuring scalability, reliability, and performance.
• Advanced Machine Learning:
• Leveraging your expertise in machine learning libraries, algorithms, and neural networks to create intelligent models.
• Implementing cutting-edge machine learning techniques for problem-solving and decision-making.
• ML Ops and Full-Stack Data:
• Championing ML Ops practices, including model deployment, monitoring, and continuous improvement.
• Working within Azure cloud to build robust AI infrastructure.
• Handling large-scale data, from data modeling to interpretation, to extract valuable insights.
• Generative AI and Natural Language Processing (NLP):
• Evaluating and implementing generative AI technologies, including LLM and multimodal tools.
• Applying NLP techniques for text understanding, sentiment analysis, and language generation.
• Communication:
• Documenting and explaining AI solutions to non-technical stakeholders.
• Continuous Learning:
• Staying up-to-date with the latest advancements and trends in AI, attending conferences, and contributing to the AI community.

Profile

• Formal Education:
• Good to have: Masters degree in a relevant field (such as Computer Science, Data Science, Artificial Intelligence, Machine Learning, or Statistics).
• Technical Skills:
• Mandatory: Proven experience (min. 5 years) as an AI Specialist or in a similar role.
• Mandatory: Strong understanding of data structures, data modeling, and software architecture.
• Mandatory: Proficiency in machine learning frameworks (such as Keras or PyTorch or TensorFlow) and libraries (like scikit-learn, Pandas and Numpy).
• Mandatory: Programming skills in Python.
• Mandatory: Very high proficiency with Azure (OpenAI Services, Synapse, Graph, Fabric, Custom Copilots,)
• Mathematical Competence:
• Good to have: Completion of at least four full mathematics modules during (under)graduate studies, including topics like algebra, analysis, and stochastic.
• Good to have: Familiarity with topics such as numerics, mathematical modeling, programming, and optimization theory.
• Mandatory: Very good proficiency in English, both written and spoken, ability to communicate about machine learning outcomes, ability to identify the right KPIs to measure and report quality and functional level of AI