Description

You will bridge the gap between data science prototypes and scalable, production-ready machine learning systems.

Responsibilities

• Convert ML models from prototypes into scalable, production-ready solutions.
• Build and maintain CI/CD pipelines for the continuous deployment of ML models.
• Implement distributed systems and cloud architectures to ensure low latency and high availability.
• Develop monitoring systems to track model performance and detect data drift.
• Optimize models for performance, scalability, and resource efficiency.

Required Skills

• 5+ years of experience in machine learning engineering or software engineering with a heavy ML focus.
• Proficiency in Python.
• Hands-on experience with PyTorch, TensorFlow, or Scikit-Learn.
• Experience with containerization using Docker and Kubernetes.
• Proficiency with AWS or GCP, including managed services like SageMaker or Vertex AI.
• Knowledge of CI/CD for ML and workflow orchestration tools such as Airflow or MLflow.
• Experience with distributed computing frameworks like Spark or Dask.
• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related engineering field.

Key Skills

Education

Bachelor's degree in Computer Science