About the role
At Tarana, you will help build a cutting-edge cloud product, a management system for wireless networks, scaling to millions of devices—using modern cloud-native architecture and open-source technologies. You will be responsible for designing and implementing distributed software in a microservices architecture. This could include everything from requirements gathering (working with product management and customers) to high-level design to implementation, integrations, operations, troubleshooting, performance tuning, and scaling.
You will work as a key member of an R& D team that owns one or more services, end-to-end. There will be PoCs, customer pilots, and production releases, all in an agile engineering environment. Expect to be challenged and stretch your skills on a daily basis. Expect to meet or beat exacting standards of quality and performance. We will provide the right mentoring to make sure that you can succeed.
Requirements:
- Bachelor's degree (or higher) in computer science or a closely related field, from a reputed university (Tier 1/Tier 2).
- At least 10 years of experience in backend software development in product companies or tech startups.
- Experience with building SaaS/IoT product offerings will be a plus.
- Software development in Java and its associated ecosystem (e. g., Spring Boot, Hibernate, etc. ).
- Strong TypeScript and Node backend skills would be an advantage.
- Microservices and RESTful APIs: implementation and consumption.
- Conceptual knowledge of distributed systems clustering, asynchronous messaging, streaming, scalability and performance, data consistency, high availability, etc. would be a big plus.
- Good understanding of databases (relational, NoSQL) and caching.
- Experience with cloud-native platforms like Kubernetes will be a big plus.
- Working knowledge of network protocols (TCP/IP, HTTP) and standard network architectures, RPC mechanisms (e. g., gRPC).
- Additional skills in using generative AI in applications.
- Should be strong in retrieval fundamentals and agentic systems.
- He/She should have done some hands-on work (beyond demos and POC) and shipped an LLM-based feature to production.