remote
AI / Machine Learning Engineer - Siemens Energy
ML Engineer
Design, develop, and deploy machine learning models and pipelines using Python, TensorFlow/PyTorch, and cloud services to deliver data‑driven solutions and integrate AI features into production applications.
About the role
Key Responsibilities
- Collaborate with data scientists, software engineers, and product managers to translate business problems into scalable ML solutions.
- Design, implement, and fine‑tune machine learning models using Python libraries such as TensorFlow, PyTorch, and Scikit‑learn.
- Develop end‑to‑end data pipelines that ingest, clean, and transform raw data into features ready for model training.
- Deploy and monitor models in cloud environments (e.g., AWS) using containerization tools like Docker and orchestration platforms.
- Optimize model performance and latency for real‑time inference in production applications.
Requirements
- Strong proficiency in Python and experience with major ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
- Hands‑on experience building, training, and deploying models in cloud environments, preferably AWS.
- Familiarity with containerization (Docker) and CI/CD pipelines for ML workflows.
- Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.
- Excellent problem‑solving skills and ability to work cross‑functionally in an agile team.
Skills
pythontensorflowpytorchawsdocker