At TechBiz Global, we provide recruitment services to top clients from our international portfolio. We are currently looking for a Senior 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
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Train, fine-tune, and optimise LLMs and SLMs using GPU infrastructure.
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Build and manage end-to-end machine learning training pipelines.
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Prepare, clean, structure, and process large volumes of real-world data.
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Select appropriate models, frameworks, tools, and training approaches based on project requirements.
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Apply techniques such as supervised fine-tuning, transfer learning, prompt tuning, and parameter-efficient fine-tuning.
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Monitor model performance and improve accuracy, speed, scalability, and resource utilisation.
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Work with structured, unstructured, time-series, telemetry, log, and streaming data.
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Clearly document and explain the tools, methods, and technical decisions used throughout the model-training process.
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Collaborate with engineering, data, and business teams to move models from experimentation into production.
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Troubleshoot issues related to model quality, training stability, GPU performance, and data pipelines.
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Proven professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or similar role.
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Strong hands-on experience training or fine-tuning LLMs and/or SLMs.
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Practical experience using GPUs for AI model training.
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Strong Python programming skills.
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Experience with machine learning and deep-learning frameworks such as:
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PyTorch
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TensorFlow
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Hugging Face Transformers
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Experience with GPU-related technologies and environments, such as CUDA, distributed training, cloud GPU platforms, or GPU clusters.
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Strong understanding of model-training workflows, including data preparation, tokenisation, model selection, training, evaluation, and optimisation.
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Ability to clearly explain previous AI projects, tools used, technical challenges, and achieved results.
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Experience working with large and complex datasets.
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Good English communication skills.
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Based in Poland.
Relevant Industry Experience
Experience in one or more of the following industries or data environments would be highly valuable:
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E-commerce
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Finance or banking
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Insurance
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Healthcare or medical data
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Telemetry and IoT data
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Application or system logs
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Real-time and streaming data
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High-volume enterprise data environments
Nice to Have
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Experience with distributed model training.
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Experience with LoRA, QLoRA, PEFT, quantisation, or model compression.
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Experience with MLOps tools and model deployment.
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Knowledge of Docker, Kubernetes, MLflow, or similar technologies.
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Experience using AWS, Azure, or Google Cloud for AI workloads.
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Experience deploying AI models into production environments.
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Knowledge of data privacy, security, and governance requirements.
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