Poznań, Poznań, Greater Poland Voivodeship, Polska
Atotech Poland Sp. z o.o.
6. 7. 2025
Informacje o stanowisku
technologies-expected :
Python
R
TensorFlow
PyTorch
Scikit-learn
SQL
NoSQL
technologies-optional :
C++
AWS
Azure
Google Cloud
Docker
Kubernetes
Spark
Hadoop
about-project :
As a Machine Learning Engineer, you will be instrumental in designing, developing, and deploying robust and scalable machine learning models for our innovative DFS solutions. You will be responsible for understanding requirements, collecting and preparing data, building and evaluating ML models, and collaborating closely with cross-functional teams to integrate these models into production systems. Additionally, you will be expected to contribute to the overall ML strategy, research new algorithms, and improve existing models. In this role, you will report to the Lead of DFS Development Team.
responsibilities :
ML Model Development: Design, develop, and implement machine learning models based on requirements and real-world data, focusing on predictive maintenance and anomaly detection.
Data Preprocessing and Feature Engineering: Perform thorough data collection, cleaning, transformation, and feature engineering to prepare datasets for model training and evaluation.
Model Training and Evaluation: Train and optimize machine learning models, utilizing various algorithms and frameworks. Evaluate model performance using appropriate metrics and techniques.
Model Deployment and Integration: Deploy ML models into production environments and ensure seamless integration with existing systems and applications.
Model Monitoring and Maintenance: Implement monitoring strategies for deployed models to track performance, detect drift, and ensure ongoing reliability. Maintain and update models as needed.
Algorithm Research and Selection: Stay up-to-date with the latest advancements in machine learning research and evaluate new algorithms and techniques for potential application within DFS solutions.
Collaboration: Work closely with data scientists, software developers, product managers, and other stakeholders to understand requirements, provide technical insights, and ensure the successful delivery of ML-powered products.
MLOps Involvement: Collaborate with MLOps engineers to streamline the ML lifecycle, including continuous integration, continuous delivery, and automated testing of ML models.
Performance Optimization: Identify and implement optimizations for ML models and pipelines to improve efficiency, scalability, and resource utilization.
Knowledge Sharing: Stay up to date with the latest ML methodologies, tools, and best practices, and share knowledge with the team.
requirements-expected :
3+ years of experience in machine learning engineering or a related field with a strong foundation in model development, deployment, and MLOps, and an interest in cutting-edge ML research.
Bachelor’s or Master’s degree in computer science, Machine Learning, Statistics, or a related quantitative field, or equivalent experience.
Proven experience as a Machine Learning Engineer with a focus on practical model development and deployment.
Demonstrated ability to effectively design, implement, and evaluate machine learning solutions.
A proactive attitude and a willingness to learn and contribute to advanced ML initiatives and research.
Machine Learning Expertise: Proven ability to design, develop, and deploy machine learning models for various applications, with a focus on predictive analytics.
Programming Languages: Strong proficiency in programming languages commonly used in ML (e.g., Python, R). Experience with C++ is a plus.
ML Frameworks: Experience with popular machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Data Handling: Strong understanding of data manipulation and analysis techniques, including SQL and NoSQL databases.
Analytical and Problem-Solving Skills: Ability to analyze complex problems, identify appropriate ML solutions, and troubleshoot model-related issues effectively.
English Proficiency: Ability to read and write technical documentation and communicate effectively with colleagues in English.
Interest in MLOps: A strong desire to learn and contribute to MLOps practices and infrastructure.
