Role overview
A Data Scientist specialized in Machine Learning and Generative AI, experienced in Azure cloud environments and focused on designing, evaluating, and monitoring intelligent agents within AI governance frameworks. Brings strong expertise in Python using pandas, scikit-learn, PyTorch or TensorFlow for supervised and unsupervised learning, robust model validation, experimentation pipelines, and deployment. Combines hands‑on work with LLMs including prompt engineering, RAG architectures, fine‑tuning, model adaptation, and systematic evaluation of generative output quality.
Supported by proven experience operating production models in Microsoft Azure through Azure Machine Learning, Azure OpenAI, ML pipelines, and Azure Storage, with a solid understanding of AI governance and Responsible AI principles such as risk assessment, bias detection, explainability through SHAP or LIME, model monitoring, and MLOps practices. Desirable experience in regulated industries, autonomous or multi‑agent systems, and AI regulatory frameworks like the EU AI Act.
The profile demonstrates strengths in scalable experimentation workflows, structured evaluation methodologies, operational reliability, and consistent delivery of high‑quality models across enterprise environments, ensuring resilience, traceability, compliance readiness, and long‑term value in AI‑driven ecosystems. Ensures adaptability to evolving regulations and business needs, suitable for complex environments requiring reliability and governance, reinforcing operational robustness and strategic alignment.
Tasks:
- Design, evaluate, and monitor intelligent agents and Machine Learning / Generative AI models within AI governance frameworks.
- Develop supervised and unsupervised models using Python, pandas, scikit-learn, PyTorch or TensorFlow.
- Build and maintain experimentation pipelines, robust validation workflows, and deployment processes.
- Perform advanced work with LLMs: prompt engineering, RAG architectures, fine‑tuning, model adaptation, and generative output evaluation.
- Operate production models through Azure Machine Learning, Azure OpenAI, ML pipelines, and Azure Storage.
- Apply AI governance and Responsible AI practices including risk assessment, bias detection, explainability (SHAP, LIME), model monitoring, and MLOps.
- Ensure resilience, traceability, compliance readiness, and operational reliability in enterprise and regulated contexts.
- Develop scalable experimentation workflows and structured evaluation methodologies.
- Remote (EU).
Rate:
- 270 €/day.