ML Platform Engineer - Work From Home

Title - ML Platform Engineer

Location: India

Work mode: Remote

Job Type: Immediate to 2 Weeks

Required skills

• 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.

Roles & responsibilities:

• Evaluate and select appropriate 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 role is crucial in bridging the gap between data science and operations, enabling

organizations to efficiently develop, deploy, and maintain machine learning models at scale.