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Site Reliability Engineer

3 months ago

Bangalore Metropolitan Area, India Quantzig Full time

Job Summary:

As a Site Reliability Engineer (SRE) specializing in Machine Learning and AI Platform, you will play a critical role in designing, implementing, and maintaining a highly scalable, reliable, and performant infrastructure to support our organization's machine learning and artificial intelligence initiatives. You'll collaborate closely with cross-functional teams including data scientists, software engineers, and product managers to ensure our ML/AI platform meets the highest standards of reliability, availability, and efficiency.


Key Responsibilities:


1. Main Role as SRE:

- Design and implement robust, scalable, and automated infrastructure solutions to support our machine learning and artificial intelligence workloads.

- Proactively identify and address potential performance bottlenecks, reliability issues, and security vulnerabilities in the ML/AI platform.

- Collaborate with AI engineering teams to define best practices for deploying, monitoring, and managing machine learning models and pipelines in production environments.

- Continuously optimize infrastructure components for cost-effectiveness, scalability, and performance.

- Optimize platform performance and ensure security and compliance standards are met

- Collaborate with cross-functional teams to troubleshoot and resolve platform-related issues

- Provide technical guidance and mentorship to junior team members

- Create, govern and continuously improve IaC automation framework and scripts for our company wide solutions

- Provide direct technical design and delivery support for top priority initiatives while governing, influencing and approving all other initiatives

- Provide support and guidance across the company on technical design and standards

- Ensure that delivered solutions are aligned to enterprise standards (Architecture, Operations, and Infrastructure) and of high quality while maintaining the required non-functional attributes such as performance, supportability, security, usability, reliability and stability.



2. Deliverables:

- Help to architect and deploy highly available and fault-tolerant infrastructure for hosting machine learning models, training pipelines,

- Implement automated deployment pipelines for deploying ML/AI models and pipelines into production environments.

- Develop and maintain monitoring and alerting systems to ensure the health and performance of the ML/AI platform.

- Create documentation and provide training to internal teams on best practices for operating and troubleshooting the ML/AI platform.

- Contribute to the development of internal tools and frameworks to streamline machine learning workflow processes.


Qualifications:


Level of educational attainment required:

  • 5-10 year of experience
  • Academic Degree – BE or BTech, MCA, M.Sc.
  • Engineer, IT-Related professions


- Extensive experience in designing, implementing, and managing cloud-based infrastructure solutions, preferably on platforms such as Azure, GCP is a plus.

- Proficiency in containerization technologies such as Docker and orchestration frameworks like Kubernetes.

- Strong programming skills in languages such as Python, Terraform.

- Experience with monitoring and observability tools such as Prometheus, Grafana, and ELK stack.

- Excellent problem-solving and communication skills, with a proactive and collaborative approach to working in cross-functional teams.