Key Responsibilities: Design, develop, and deploy machine learning models with a focus on generative AI (e.g., LLMs, diffusion models, GANs). Apply generative AI for use cases such as text generation, summarization, classification, synthetic data creation, and intelligent assistants. Collaborate with data engineers and ML engineers to build scalable pipelines. Lead experimentation and validation of new GenAI models and tools. Support prompt engineering and fine-tuning of foundation models where applicable. Ensure compliance with data privacy, security, and ethical AI standards. Present findings and models to stakeholders in clear, actionable formats. Required Skills & Experience: 5 years of experience as a Data Scientist or ML Engineer. Strong proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers, LangChain). Deep knowledge of foundational models, LLMs (GPT, Claude, LLaMA, Mistral, etc.), and associated tooling. Hands-on experience with fine-tuning or customizing generative AI models. Solid grounding in classical ML (supervised/unsupervised learning), statistics, and NLP. Familiarity with deploying models via REST APIs, Docker, and cloud services (e.g., AWS, Azure, GCP). Experience working with enterprise data in complex environments (structured/unstructured). Strong problem-solving skills and ability to communicate technical concepts to non-technical audiences. Nice to Have: Experience with vector databases (e.g., FAISS, Pinecone, Weaviate). Familiarity with GenAI enterprise platforms (e.g., OpenAI, Azure OpenAI, Cohere, Anthropic). Experience in regulated industries (e.g., healthcare, finance, public sector). Knowledge of European data protection regulations (e.g., GDPR).