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Senior Engineer, EMS & AI Automation at Parallel Wireless | Swiftcruit
Parallel Wireless is a U.S.-based pioneer in Open RAN innovation, transforming how mobile networks are built, optimized, and powered. Through our GreenRAN™ portfolio, we enable operators to deliver next-generation connectivity with unmatched energy efficiency, automation, and flexibility.
What you need -
8+ years of professional software development experience, with 3+ years of production Go (Golang)
Strong grasp of Go idioms: goroutines/channels, context propagation, interfaces and composition, error handling
Hands-on experience with Kafka or similar messaging systems in event-driven architectures
Experience designing and building RESTful APIs in distributed microservice systems
Strong understanding of concurrency, high availability, and multi-replica deployment concerns (shared state, locking, idempotency)
Working knowledge of AI-assisted development tools across the SDLC — using AI coding assistants and agents (e.g., for code generation, refactoring, test writing, debugging, and code review) to improve development speed and quality, with sound judgment on validating and reviewing AI-generated output.
Familiarity with device management protocols: TR-069 (CWMP), NETCONF/YANG, SNMP, O1
Exposure to 3GPP or O-RAN specifications and OAM/FCAPS concepts
What you will do -
Design, develop, and maintain Go microservices for RAN device configuration, fault management, and network optimization
Build and evolve protocol adapters that translate between internal data models and device-facing protocols
Design MongoDB schemas, DAOs, and efficient queries (projections, indexes, aggregations, change streams) for a sharded production cluster
Develop event-driven flows using Kafka for faults, cell-state changes, and configuration propagation across services
Ensure services are HA-ready: multi-replica safe, stateless where possible, with graceful shutdown and proper concurrency control
Write meaningful unit tests, participate in code reviews, and uphold security, performance, and error-handling standards
Debug production issues across services using logs, metrics, and distributed tracing of data flows
Leverage AI tools throughout the SDLC — from design and coding to testing, documentation, and troubleshooting — to accelerate delivery while ensuring output is reviewed, validated, and production-ready.
