onsite
Machine Learning Engineer - Applied Information Sciences
ML Engineer
Design, develop, and deploy machine‑learning models and pipelines using Python, deep‑learning frameworks, and cloud services to solve real‑world client problems.
About the role
Key Responsibilities
- Develop, train, and fine‑tune machine‑learning and deep‑learning models using Python, TensorFlow, PyTorch, and Scikit‑learn.
- Build scalable data pipelines and model serving infrastructure on AWS, leveraging services such as SageMaker, EC2, and S3.
- Containerize applications with Docker and orchestrate deployments to ensure reproducibility and rapid iteration.
- Collaborate with data engineers, product owners, and domain experts to translate business requirements into robust ML solutions.
- Monitor model performance in production, implement continuous improvement processes, and document best practices.
Requirements
- Bachelor’s or higher in Computer Science, Engineering, Mathematics, or a related field with 2+ years of hands‑on ML experience.
- Proficiency in Python and major ML libraries (TensorFlow, PyTorch, Scikit‑learn).
- Experience deploying models on AWS cloud platforms and using Docker for containerization.
- Strong understanding of data preprocessing, feature engineering, and model evaluation techniques.
- Excellent problem‑solving skills and ability to work collaboratively in an agile environment.
Skills
pythontensorflowpytorchawsdocker