Data Scientist

At TechBiz Global, we provide recruitment services to top clients from our international portfolio. We are currently looking for aSenior Data Scientist with 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.

The ideal candidate should be based in Poland and 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.

Key Responsibilities

• Train, fine-tune, and optimise LLMs and SLMs using GPU infrastructure.

• Build and manage end-to-end machine learning training pipelines.

• Prepare, clean, structure, and process large volumes of real-world data.

• Select appropriate models, frameworks, tools, and training approaches based on project requirements.

• Apply techniques such as supervised fine-tuning, transfer learning, prompt tuning, and parameter-efficient fine-tuning.

• Monitor model performance and improve accuracy, speed, scalability, and resource utilisation.

• Work with structured, unstructured, time-series, telemetry, log, and streaming data.

• Clearly document and explain the tools, methods, and technical decisions used throughout the model-training process.

• Collaborate with engineering, data, and business teams to move models from experimentation into production.

• Troubleshoot issues related to model quality, training stability, GPU performance, and data pipelines.

• Proven professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or similar role.

• Strong hands-on experience training or fine-tuningLLMs and/or SLMs.

• Practical experience usingGPUs for AI model training.

• StrongPython programming skills.

• Experience with machine learning and deep-learning frameworks such as:

  • PyTorch

  • TensorFlow

  • Hugging Face Transformers

• Experience withGPU-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.
  

Relevant Industry Experience

Experience in one or more of the following industries or data environments would be highly valuable:

• 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

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.