Data Scientist

Key Responsibilities

• check_circle Train, fine-tune, and optimise LLMs and SLMs using GPU infrastructure.
• check_circle Build and manage end-to-end machine learning training pipelines.
• check_circle Prepare, clean, structure, and process large volumes of real-world data.
• check_circle Select appropriate models, frameworks, tools, and training approaches based on project requirements.
• check_circle Apply techniques such as supervised fine-tuning, transfer learning, prompt tuning, and parameter-efficient fine-tuning.
• check_circle Monitor model performance and improve accuracy, speed, scalability, and resource utilisation.
• check_circle Work with structured, unstructured, time-series, telemetry, log, and streaming data.
• check_circle Clearly document and explain the tools, methods, and technical decisions used throughout the model-training process.
• check_circle Collaborate with engineering, data, and business teams to move models from experimentation into production.
• check_circle Troubleshoot issues related to model quality, training stability, GPU performance, and data pipelines.

Basic qualifications

• 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:
• 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.
• Based in Poland.

Nice to Have

• 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.

About the company