Senior Software Engineer, AI/ML, GCP

1 - 5 years

0 Lacs

Posted:2 weeks ago| Platform: Shine logo

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On-site

Job Type

Full Time

Job Description

As a software engineer at Google Cloud, you will be developing next-generation technologies that impact billions of users worldwide. You will have the opportunity to work on critical projects, collaborate with internal teams, and innovate to meet customer needs. Your responsibilities will include: - Partnering with customers to measure AI/ML model performance on Google Cloud infrastructure. - Identifying and resolving technical bottlenecks to ensure customer success. - Collaborating with internal infrastructure teams to enhance support for demanding AI workloads. - Developing and delivering training materials and demos for customers and internal teams. - Contributing to product improvement by identifying bugs and recommending enhancements. - Writing and testing production-quality code for system development and deployment. - Conducting performance profiling, debugging, and troubleshooting of training and inference workloads. - Performing design and code reviews to ensure adherence to best practices. - Troubleshooting and resolving system issues by analyzing root causes. - Designing and implementing specialized ML solutions using advanced infrastructure. Minimum qualifications: - Bachelor's degree in Computer Science or a related technical field. - 5 years of experience in software development using one or more programming languages. - 2 years of experience with ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging. - 1 year of experience in ML performance, large-scale systems data analysis, ML debugging, Large Language Models (LLMs), or a related ML field. Preferred qualifications: - Experience in data structures and algorithms. - Ability to manage product improvement through bug fixes and feature enhancements. - Ability to develop advanced ML/AI infrastructure training materials and demos. - Collaboration with internal infrastructure teams to identify bottlenecks and expand capacity.,

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