Machine Learning Engineer

- Model Deployment and Monitoring - Deploy machine learning models into production environments and implement robust monitoring solutions for performance and reliability.

- Infrastructure Management - Work closely with DevOps and IT teams to maintain and enhance the infrastructure required for machine learning operations, leveraging cloud platforms and containerization technologies.

- Automation - Develop and maintain automation scripts and tools for model deployment, scaling, and monitoring to streamline machine learning operations workflows.

- Collaboration - Collaborate with data scientists, software engineers, and other stakeholders to understand model requirements, integration needs, and performance expectations.

- Troubleshooting - Investigate and resolve issues related to machine learning model deployment, performance, and scalability in a timely manner.

- Security and Compliance - Ensure machine learning operations adhere to security and compliance standards, implementing best practices for data privacy and model governance.

Skills & Background

- Experience in deploying and maintaining machine learning models in production environments.

- Proficiency in cloud platforms

- Strong programming skills in languages Python

- Experience with relevant frameworks (e.g., TensorFlow, PyTorch).

- Familiarity with containerization technologies (Docker, Kubernetes).

- Solid understanding of DevOps practices and tools.

- Experience with continuous integration and continuous deployment (CI/CD) pipelines.

- Knowledge of machine learning model serving platforms.