Senior Software Engineer, ML Infrastructure

2 weeks ago

Bengaluru, Karnataka, India Google Full time ₹ 12,00,000 - ₹ 36,00,000 per year
Minimum qualifications:
  • Bachelor's degree or equivalent practical experience.
  • 5 years of coding experience in one or more of the following languages: C, C++, Java, or Python.
  • 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
  • Experience with Cloud Security and Compliance.
  • Experience building distributed systems.
  • Experience in building Machine Learning Operations platforms.
Preferred qualifications:
  • Master's or PhD in Computer Science or related technical field.
  • Experience working with Graphics Processing Unit, Tensor Processing Unit, or High Performance Computing (HPC) clusters.
  • Experience in ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn.
  • Experience with ML operation principles and tools for model versioning, monitoring, and deployment.
  • Knowledge of open source data analytics tools such as Apache YARN, Spark, Hive, Flink etc.
About the job:
Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities:
  • Make Cloud Dataproc the leading product for data science, ML and AI workloads.
  • Design and build software stack to leverage Google technologies for cluster setup, excellent AI/ML operations, monitoring and observability.
  • Engage with Google Cloud Platform (GCP) infrastructure teams to deliver the GCP features to Dataproc customers.
  • Build customer-facing features which make Cloud Dataproc the leading platform to run Hadoop, Spark, Flink, AI/ML and newer technologies in the cloud.
  • Work across boundaries in a distributed team.

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