AI/ML Developer (Chemistry, Industry)

• Degree in Computer Science, Engineering, or a related technical field
• Proficiency in Polish and English (spoken and written)
• Strong programming skills (e.g., Python) and experience with ML/AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Hands-on experience building and deploying machine learning models in production environments
• Solid understanding of machine learning techniques, including: Generative AI, Computer vision, Classification and regression
• Experience working with time-series data and industrial datasets
• Familiarity with industrial systems (SCADA, MES, ERP, SAP, IoT)
• Experience with data processing, storage, and pipeline development
• Knowledge of MLOps practices (CI/CD for ML, model monitoring, versioning)
• Ability to write clean, maintainable, and well-documented code
• Strong problem-solving skills and ability to work independently on technical challenges
• Willingness to work closely with business teams to shape and refine AI use cases
• Strong communication skills and ability to collaborate effectively with business stakeholders

Nice to have:

• Experience in Chemistry or industrial/manufacturing domains
• Familiarity with cloud platforms (AWS, Azure, GCP)
• Experience with real-time data processing and streaming systems
• Understanding of production environments and operational metrics in factories
• Experience supporting or participating in pre-sales activities (e.g., technical input to proposals, solution discussions)

• Design, develop, and deploy machine learning and AI solutions, including Generative AI, computer vision, and predictive models
• Build scalable data pipelines and integrate models into production environments
• Collaborate with data engineers and scientists to process and analyze large datasets (including industrial and time-series data)
• Translate business requirements into technical implementations, focusing on robust and maintainable solutions
• Develop and optimize algorithms for real-world industrial use cases (e.g., predictive maintenance, anomaly detection, process optimization)
• Work with industrial data sources such as SCADA, MES, ERP, SAP, and IoT systems
• Implement model monitoring, validation, and continuous improvement pipelines (MLOps practices)
• Contribute to system architecture with a focus on performance, scalability, and reliability
• Prototype and experiment with new AI approaches, rapidly iterating toward production-ready solutions
• Support integration of AI systems into existing client infrastructure and applications