Cloud Native Machine Learning Architect
2 days ago
AKSHAYA BUSINESS IT SOLUTIONS PRIVATE LIMITED is seeking an experienced Cloud Native Machine Learning Architect to join our team. As a key member of our GenAI delivery organization, you will lead a team of skilled engineers across various disciplines and play a crucial role in building cutting-edge Machine Learning (ML) platforms for our clients.
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.
Estimated Salary Range: $220,000 - $280,000 per year
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.
- 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.
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