Senior Engineer, EMS & AI Automation

Job requirements -

• 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
• Solid experience with MongoDB (or comparable NoSQL): schema design, indexing, aggregation pipelines, replica sets/sharding
• 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.
• Preferred / Nice to Have
• Telecom domain experience: RAN, EMS/NMS, O-RAN architecture (Near-RT RIC, SMO), 4G/5G
• Familiarity with device management protocols: TR-069 (CWMP), NETCONF/YANG, SNMP, O1
• Exposure to 3GPP or O-RAN specifications and OAM/FCAPS concepts

Candidate responsibility -

• 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.