Senior AI Engineer/ Data Scientist

We are seeking a highly skilled and experienced Senior AI Engineer/ Data Scientist to join our dynamic group. In this pivotal role, you will be responsible for designing, developing, and implementing cutting-edge (Generative) AI solutions leveraging Python, Machine Learning, Large Language Models (LLMs), and classical Natural Language Processing (NLP) methodologies. You will collaborate closely with two expert teams to analyze large datasets, build robust models, and create innovative solutions. Additionally, you will play a key role in guiding junior colleagues and may take on topic leadership for specific areas. This position demands a high degree of adaptability and willingness to contribute to both short-term, agile projects and longer-term strategic developments, such as an in-house developed chat system. Your contribution will be crucial in ensuring we always have up-to-date and demonstrable solutions readily available for pre-sales activities and rapid prototyping. Key Responsibilities: Design & Development: Design, develop, and implement advanced machine learning algorithms, models, and LLM-based pipelines. Data Analysis & Insights: Analyze large datasets to extract meaningful insights and patterns. Software Engineering: Write production-quality code in Python, adhering to standard software engineering best practices (Git, testing, etc.). A strong focus will be on backend development using FastAPI and leveraging Azure SDKs. Support basic front-end development tasks. Collaboration & Problem Solving: Collaborate effectively with cross-functional teams to identify, understand, and solve complex business problems. Technology Adoption: Stay up to date with the latest advancements in AI, GenAI, and data science, actively integrating new technologies where beneficial. Integration & Deployment: Work closely with software engineers to integrate AI models into production systems and package AI components for reusability. Mentorship & Leadership: Provide guidance and mentorship to junior data scientists and engineers, potentially leading specific topics or initiatives. MLOps & Infrastructure: Contribute to the development of robust MLOps pipelines (CI/CD for ML) and apply strong skills in Docker and container orchestration. Strategic Evaluation: Research and evaluate external AI services versus in-house development ("Make or Buy") in a global context, assessing feasibility and ROI. Agile Contribution: Support highly volatile projects on demand and contribute to rapid prototyping and continuous improvement. Develop use cases and demos for pre-sales purposes, ensuring consistently updated versions.