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Software Development Engineer | McAfee



Location: Bengaluru

Type: Full time

About the Role

Cloud Software Development Engineer with ability to design, develop, deploy, and maintain cloud-based applications and services, leveraging cloud platforms AWS and GCP focusing on scalability, security, and efficiency. This is a Hybrid position located in Bangalore. You will be required to be onsite on an as-needed basis, typically 1 to 6 times a month. We are only considering candidates within a commutable distance and are not offering relocation assistance at this time

Requirements

  • Proficient in programming languages such as Python and JAVA, with experience understanding of unit test frameworks in Python & JAVA.
  • Strong understanding of cloud platforms, particularly AWS and Google Cloud Platform (GCP), and their services.
  • Solid grasp of AI and ML concepts and best practices.
  • Understanding of end-to-end ML lifecycle and workflows in production-grade environments.
  • Knowledge of cloud security best practices and the ability to implement them in application and infrastructure design.
  • Familiarity with containerization and orchestration technologies such as Docker and Kubernetes.
  • Competent in working with both relational and non-relational databases to support dynamic application needs.

Responsibilities

  • Develop and deploy cloud-based distributed applications, ensuring they are efficient, secure, and scalable.
  • Build and optimize large-scale data processing pipelines using Spark/pySpark, integrating with cloud-native services.
  • Monitor the performance and health of cloud-based applications and infrastructure, ensuring they meet performance, scalability, and security standards.
  • Collaborate with developers, SDETs, Researchers, DevOps engineers, and system administrators to ensure smooth development, deployment, and operations of data-driven applications.
  • Implement data analytics and dashboarding solutions to support business intelligence, operational monitoring, and model performance tracking.
  • Contribute to the development, deployment, and monitoring of machine learning models, with a clear understanding of the end-to-end ML lifecycle, including data collection, preprocessing, model (re)training, evaluation, deployment, and feedback loops.
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