Machine Learning Infrastructure Specialist
2 days ago
We are a leading provider of cutting-edge Machine Learning solutions for Fortune 500 clients. Our team is seeking a highly skilled Machine Learning Platform Engineer to lead the development and deployment of our MLOps platform.
Job Summary:The successful candidate will be responsible for designing, implementing, and maintaining a scalable and secure MLOps platform that enables efficient model training, deployment, and monitoring. This role requires strong technical expertise in cloud services, DevOps, and machine learning workflows.
Key Responsibilities:- Evaluate and select suitable cloud services for each stage of the ML lifecycle.
- Design and implement the overall architecture of the MLOps platform.
- Set up automated pipelines for data preparation, model training, and deployment.
- Implement version control for code, data, and models.
- Ensure the platform is scalable, secure, and compliant with relevant regulations.
- Provide tools and interfaces for data scientists to easily leverage the platform.
- Continuously optimize the platform for performance and cost-efficiency.
This is a unique opportunity to work with a talented team and contribute to the development of innovative Machine Learning solutions. If you have a passion for technology and a drive to succeed, we encourage you to apply.
Requirements:- 10+ years of professional experience in building applications using cloud services.
- Prior experience in building Machine Learning platforms using cloud services.
- Cloud expertise: Deep knowledge of cloud platforms like AWS, Google Cloud Platform, or Azure, including their machine learning and data services (Azure preferred).
- DevOps skills: Experience with CI/CD pipelines, infrastructure as code, and containerization technologies like Docker and Kubernetes.
- Machine learning knowledge: Understanding of ML workflows, model training, and deployment processes.
- Data engineering: Familiarity with data pipelines, ETL processes, and data storage solutions.
- Software engineering: Strong programming skills, particularly in languages commonly used in ML like Python.
- System design: Ability to architect scalable, reliable systems that integrate various services.
- Automation: Expertise in automating workflows and processes across the ML lifecycle.
- Security and compliance: Knowledge of best practices for securing ML pipelines and ensuring regulatory compliance.
- Monitoring and logging: Experience setting up monitoring and logging for ML systems.
- Collaboration: Ability to work with data scientists, software engineers, and other stakeholders.
A competitive salary range of $180,000 - $250,000 per year, depending on experience.
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