Data Scientist
Remote
Job Summary
Train, fine-tune, and optimise Large and Small Language Models using GPU infrastructure while building end-to-end machine learning training pipelines. Prepare, clean, and process large volumes of real-world structured, unstructured, and streaming data to select appropriate frameworks and apply techniques such as supervised fine-tuning and parameter-efficient fine-tuning. Monitor model performance to improve accuracy, speed, scalability, and resource utilisation, then collaborate with engineering and business teams to deploy models into production. Troubleshoot issues related to model quality, training stability, and GPU performance while clearly documenting technical decisions and explaining tools used throughout the process. Based in Poland with strong Python and PyTorch skills, the candidate will work across industries including finance, healthcare, and e-commerce to handle complex datasets and support AI workloads on cloud platforms.
Required Qualifications
- Senior Data Scientist
- strong hands-on experience in training AI models, particularly Large Language Models (LLMs) and Small Language Models (SLMs), using GPU infrastructure and real-world datasets
- based in Poland
- able to clearly demonstrate their technical expertise, explain the tools and frameworks they use, and describe the complete model-training process—from data preparation to deployment and performance optimisation
- Proven professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or similar role
- Strong hands-on experience training or fine-tuning LLMs and/or SLMs
- Practical experience using GPUs for AI model training
- Strong Python programming skills
- Experience with machine learning and deep-learning frameworks such as: PyTorch, TensorFlow, Hugging Face Transformers
- Experience with GPU-related technologies and environments, such as CUDA, distributed training, cloud GPU platforms, or GPU clusters
- Strong understanding of model-training workflows, including data preparation, tokenisation, model selection, training, evaluation, and optimisation
- Ability to clearly explain previous AI projects, tools used, technical challenges, and achieved results
- Experience working with large and complex datasets
- Good English communication skills
Desired Qualifications
- Experience in one or more of the following industries or data environments: E-commerce, Finance or banking, Insurance, Healthcare or medical data, Telemetry and IoT data, Application or system logs, Real-time and streaming data, High-volume enterprise data environments
- Experience with distributed model training
- Experience with LoRA, QLoRA, PEFT, quantisation, or model compression
- Experience with MLOps tools and model deployment
- Knowledge of Docker, Kubernetes, MLflow, or similar technologies
- Experience using AWS, Azure, or Google Cloud for AI workloads
- Experience deploying AI models into production environments
- Knowledge of data privacy, security, and governance requirements
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.