Netradyne

Staff Software Engineer - Cloud

Bangalore Division, KA, IN

12 days ago
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Summary

Position: Staff Software Engineer

Location: Bangalore, India (Onsite)

Experience: 6+ years

Domain: Cloud, Data Platforms, and AI Integration



Role Overview:

We are looking for a Staff Software Engineer with expertise in building scalable, cloud-native platforms and data processing pipelines. This role offers the opportunity to work at the intersection of large-scale data engineering and next-generation AI capabilities, including Generative AI and LLMs.


Key Responsibilities.


  • Design and develop scalable cloud and web components to ingest, process, transform, and visualize large-scale data.
  • Build and maintain robust data pipelines to support data processing and machine learning workflows.
  • Develop pipelines for training, validating, and deploying ML models.
  • Collaborate with cross-functional teams to integrate GenAI capabilities, such as LLMs and intelligent agents, into production systems.
  • Design software systems that support iterative development, testing, and debugging of algorithms and AI models.
  • Optimize data processing performance and infrastructure reliability.
  • Follow engineering best practices, including unit testing, code reviews, and continuous integration/delivery.
  • Deploy, monitor, and maintain production-grade infrastructure and services.


Requirements:


  • Strong analytical, problem-solving, and organizational skills with keen attention to detail.
  • 6+ years of programming experience in Java, Python, or Go.
  • Hands-on experience working with at least one major cloud platform: AWS, Azure, or Google Cloud Platform (GCP).
  • Proficient in working with RDBMS (e.g., PostgreSQL, MySQL) or NoSQL (e.g., MongoDB, Cassandra) databases.
  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Experience working with streaming data frameworks (e.g., Kinesis/Kafka/Flink is a plus.
  • Exposure to Generative AI, LLMs, and AI agents, with a solid understanding of MCPs and Agentic frameworks

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