Research and Innovation: Stay up to date with the latest advances in AI + ML to be able to provide good advice to the Product Owner and offer a set of top-notch ML techniques in the NLP field, mainly.
Development: Train and deploy machine learning models; Design, develop, and deploy methods and techniques to achieve desired goals. colleagues of all types to identify opportunities to leverage machine learning to solve complex problems and improve user experience.
Strategy and Vision: Develop and execute a roadmap for ML initiatives aligned with customer/ Product Owner /platform objectives.
Functional Collaboration: Work with business teams and colleagues of all types to identify opportunities to leverage AI+ML techniques to solve complex problems and improve user experience.
Vector Database Management: Configure and manage vector databases to efficiently store and retrieve multidimensional data, enabling fast and accurate similarity searches.
Data Management: Collect, preprocess, and analyze large-volume datasets to feed model development and extract insights.
ML Feature Engineering: Perform ML feature engineering to improve model performance and accuracy.
Model Training: Train and fine-tune machine learning models using advanced techniques, in combination with LLMs.
Model Optimization: Evaluate and optimize model performance to ensure accuracy, efficiency, and scalability.
Testing and Maintenance: Implement and maintain robust testing frameworks to ensure the reliability and proficiency of machine learning solutions.
Documentation: Document processes, models, and methodologies to ensure knowledge sharing and reproducibility.
Requirements & Skills:
Initial training: Engineer or Master in computer science, Data Science, machine learning, or an equivalent field.
Experience: Proven experience as an AI+ML engineer or similar role, with a strong portfolio of successful projects.
Prior experience in the Health Sector and RWE is a plus ( Real World Evidence ).
Technical skills: Proficiency in programming languages such as Python (mandatory), R or similar. Experience with ML frameworks and libraries such as TensorFlow, PyTorch, and the classics: sci-kit-learn, etc, containing those of NLP.
Fundamentals: Solid understanding of data structures, algorithms, and software engineering principles + good foundation in applied mathematics and statistics as a traditional data scientist.
NLP Expertise: Knowledge of natural language processing (NLP) techniques and tools.
Vector Databases: Experience working with vector databases (e.g. Pinecone, FAISS, Milvus) for storage, similarity searches, and working on recommendation engines.
LLM and AI Capability: Knowledge and hands-on experience in generative AI, primarily text-based, are required. Experience in developing AI+ML solutions with a focus on LLMs and LLM frameworks is highly valued.
