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Adentis Portugal ищет инженера по генеративному ИИ для разработки и внедрения моделей, включая LLM и мультимодальные подходы. Требуется опыт работы с пайплайнами данных, fine-tuning и RAG-техниками, а также знание Python и инструментов PyTorch/TensorFlow.
Кандидат будет взаимодействовать с командами DE и IT, обеспечивая мониторинг, безопасность и масштабируемость решений в облачной среде Azure. Готовность к работе в гибком режиме и сотрудничеству с командой в португальских проектах
With just over 10 years of experience in the Portuguese market, we share our DNA with more than 200 workers and position our offer according to 3 lines of service: Strategy (Outsourcing, NeXel, Team as a Service, Tech Academies); R&D (Bootcamps, POC, Tech Lab); Nearshore.
Develop and implement generative AI models, including LLMs and multimodal architectures, for use cases such as content personalization, intelligent automation, semantic analysis, and assisted creation. Create and manage automated data pipelines and fine-tuning workflows, ensuring efficient data processing, dataset preparation, and seamless integration of generative models into existing systems and workflows. Enhance platform monitoring capabilities in alignment with the Data Science team to ensure the quality, consistency, and reliability of data used for training, evaluation, and inference of generative models. Collaborate with Data Engineering (DE) and IT teams to identify and leverage data platforms that support the development of feature stores, pipelines, and dataset management for generative AI workloads. Advance the MLOps/LLMOps discipline to include model versioning (model registry), dataset versioning, prompt management, and experiment tracking to support a robust experimentation environment. Utilize Azure Databricks for data processing, model training, fine-tuning, and deployment, optimizing resources for efficient and cost-effective generative AI solutions. Design and maintain scalable, high-performance infrastructure required to serve generative models in production environments. Work closely with IT and DevOps teams to manage cloud resources and deployment environments, ensuring security, scalability, and performance of generative AI applications.