We are ramping-up newly created software team in Kraków that is developing solutions and service applications for installed base management, assessment of substations, maintenance planning and recommended actions, user account management and safety applications.
As a Machine Learning Engineer you will act as a receiver of the statistical models from research projects. You will use existing infra from big vendors like Microsoft/AWS and will adopt consolidated techniques. We look for somebody who have reliability/statistical modelling capacity (not pure AI) complemented with sound experience in AI/ML and someone who is more an expert in applied AI with Mechatronic/Robotics education.
responsibilities :
Implement predictive and prescriptive (recommended actions) analytics from sourcing data, modeling, to their deployment in production for cloud and on-premise applications.
Responsible for data analytics lifecycle covering data discovery, preparation & processing, data model design, model building & deployment, together with measuring its effectiveness and subsequent optimization.
In collaboration with research teams create innovative Machine Learning (ML) and Large Language Models (LLM) for AI driven applications and contribute to devise new opportunities from the various data streams.
Design, build and optimize Machine Learning (ML) and Large Language Models (LLM) for various AI driven applications.
Collaborate with cross functional teams on the development and implementation of end-to-end ML/LLM based applications, integrating them seamlessly into existing platforms and workflows.
Evaluate model performance metrics and adjust models to optimize their effectiveness
Maintain documentation of code, models, and processes.
Provide expertise and guidance on ML and LLM capabilities and potential applications to stakeholders.
requirements-expected :
Bachelor’s or Master’s degree in Data Science, Applied Mathematics, Artificial Intelligence, or a related technical field.
3 - 5 years of experience in building, deploying, and maintaining ML models in a production environment.
Demonstrated experience in training, fine-tuning, and deploying LLMs in a production environment.
Good knowledge of reliability models, statistical methods and prognostics.
Experience with data management, API integration, and cloud services (like Azure, AWS, or Google Cloud, particularly their AI and machine learning stacks) is preferrable.
Knowledge of Python, databases and main machine learning concepts, and libraries such as sklearn, pytorch/tensorflow.
Understanding of Agile principles and associated practices.
Fluency in English.
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of foreign language classes
sharing the costs of professional training & courses
life insurance
remote work opportunities
flexible working time
corporate products and services at discounted prices
