At Quantum One, we build AI systems that work in real operational environments — connected to live data, existing business processes, and enterprise decision-making.
What you will do
Design, train, evaluate, and improve machine learning models for real-world enterprise and industrial use cases.
Work with structured, unstructured, and time-series data from operational systems, business workflows, and domain-specific sources.
Build ML pipelines for data processing, feature engineering, model training, validation, deployment, and monitoring.
Collaborate with data engineers, product designers, domain experts, and client teams to translate business challenges into AI solutions.
Develop models for forecasting, anomaly detection, classification, recommendation, optimization, and decision support.
What you will bring
Strong experience with machine learning model development, evaluation, and deployment in real-world products or business environments.
Good understanding of supervised and unsupervised learning, time-series forecasting, anomaly detection, classification, recommendation systems, or optimization methods.
Ability to work with complex datasets, including structured, unstructured, and operational data from enterprise systems.
Experience building data processing, feature engineering, training, validation, and monitoring pipelines.
Practical knowledge of Python and common ML/data tools such as pandas, scikit-learn, PyTorch, TensorFlow, MLflow, Airflow, or similar.
Benefits
A chance to work on complex AI products for enterprise and industrial clients.
Direct impact on systems used for real business decisions.
A multidisciplinary team combining AI, engineering, product, design, and industry expertise.
Ownership over technical decisions and room to shape how ML is built and deployed.
Challenging problems where data, operations, and business value meet.