Senior ML/Data Scientist with focus on LLM

Wymagania: - Experience with LLM applications development in particular agentic design such as tool using and reasoning. - Experience in building data pipelines and deployment pipelines for LLM applications. - Recent experience with ML/AI toolkits such as AWS Sagemager (other toolkits like Pytorch, Tensorflow, Keras, MXNet, H20, etc are nice to have). - Experience with MLOps technologies (Sagemaker, Vertex AI, Kubeflow). - Experience with cloud solutions (AWS / Azure / GCP), docker. - Proven scripting and automation skills. - Good knowledge of: git, bash, linux, CI/CD tools (e.g. jenkins, gitlab CI), software lifecycle, RDB, visualization tools eg Tableau, Jira, confluence. - Programming languages: Python, R. - Test driven development, good coding practices. - Problem-solving and decision-making skills. - Customer & delivery focus. - Ability to work effectively with team members and virtual teams from different locations and different cultural backgrounds. Mile widziane: - Experience with LLM fine tuning a big plus. - Experience with deployment of scalable apps a plus. - Experience with clinical study data a plus. O firmie: - At Square One Poland we link IT experts with the business. With over 25 years of experience, we specialize in recruitment processes on a global scale. Despite years of experience, we still have a startup DNA and this is our advantage. Our offices are located in London and Warsaw, however, we can reach clients from all over the world, from start-ups to big worldwide corporations. Zakres obowiązków: - Design, develop, and deploy solutions based on large language models (LLM), including agent design and tool utilization for problem-solving. - Fine-tune LLM models and optimize their performance to meet specific business requirements. - Develop and optimize data pipelines and deployment pipelines for LLM-based applications. - Integrate clinical, non-clinical, and external real-world data (RWD) from various sources. - Work with ML/AI tools, including AWS SageMaker, PyTorch, TensorFlow, Vertex AI, and implement MLOps solutions using tools like Kubeflow. - Create scripts and automate processes using tools like Git, Bash, Docker, and Kubernetes. - Develop scalable applications in cloud environments (AWS, Azure, GCP). - Implement Continuous Integration / Continuous Deployment (CI/CD) practices using tools like Jenkins or GitLab CI. - Collaborate with teams across different locations and cultures to deliver customer-oriented solutions. - Test and optimize ML models, manage training and testing datasets, and mitigate overfitting.