About the job:

Total Experience - 4 Years and above. Notice Period - Immediate - 15 Days. Location - Bangalore.

Job Description :

Machine Learning Platform (MLP) is a fully managed, multi-tenant, and scalable platform designed for training and building Machine Learning models. MLP provides the latest data science tools, seamless integration, and a secure environment, delivered as Platform-as-a-Service (PaaS). As a fully managed PaaS, MLP allows data scientists and ML engineers to focus more on developing ML models and less on configuring environments or integrating system.

We are looking for a skilled Machine Learning Platform Engineer to design, build, and operate a fully managed, scalable, secure, and multi-tenant Machine Learning Platform (MLP). The MLP enables Data Scientists and ML Engineers to develop, train, and deploy machine learning models without having to manage underlying infrastructure or complex environment configurations.

In this role, you will work at the intersection of Machine Learning, Cloud Infrastructure, DevOps, Platform Engineering, and Software Engineering to build reliable and scalable platforms that accelerate the end-to-end ML lifecycle.

Key Responsibilities :

- Design, develop, and maintain a scalable and highly available Machine Learning Platform.

- Build and manage platform capabilities for ML model development, training, experimentation, and deployment.

- Develop self-service tools and workflows that enable Data Scientists and ML Engineers to efficiently provision and manage ML environments.

- Design and implement multi-tenant architecture, ensuring appropriate isolation, security, and resource management.

- Automate infrastructure provisioning, configuration, deployment, and operational processes.

- Integrate modern data science and ML tools, frameworks, and services into the platform.

- Build and maintain CI/CD pipelines for ML applications, services, and platform components.

- Implement monitoring, logging, observability, alerting, and performance-management capabilities.

- Optimize compute, storage, networking, and other cloud resources for scalability, reliability, and cost efficiency.

- Collaborate with Data Scientists, ML Engineers, Software Engineers, DevOps Engineers, and Security teams.

- Establish platform standards, reusable components, APIs, and automation frameworks.

- Ensure the platform meets enterprise requirements for security, compliance, reliability, and availability.

- Troubleshoot complex infrastructure, platform, and ML workload issues.

- Continuously evaluate emerging technologies and recommend improvements to the ML platform.