Senior Engineer, EMS & AI Automation

Parallel Wireless; Maharashtra; On-site; INR 400,000 - 600,000; Full time

Job summary

Parallel Wireless in Maharashtra, India, is seeking a senior software engineer to design, develop, and maintain Go microservices for RAN device configuration, fault management, and network optimization.

You will build protocol adapters, design MongoDB schemas for a sharded production cluster, and implement event-driven flows with Kafka. The role emphasizes high availability, stateless design, and code quality, with AI-assisted development tools across the SDLC.

Qualifications

• 8+ years of professional software development experience.
• 3+ years of production Go experience.
• Strong Go idioms and error handling.
• Experience with MongoDB and message-driven systems.
• Experience designing RESTful APIs in microservices.

Responsibilities

• Design, develop, and maintain Go microservices for RAN configuration and network optimization.
• Build protocol adapters between internal data models and device-facing protocols.
• Design MongoDB schemas and queries for a sharded production cluster.
• Develop event-driven flows using Kafka for faults and state changes.
• Ensure HA readiness with stateless services and proper concurrency control.
• Write unit tests, perform code reviews, maintain security and performance.
• Leverage AI tools across the SDLC to accelerate delivery with validated outputs.

Skills

Go (Golang)

REST APIs

Concurrency control

AI-assisted development tools

O-RAN concepts

Tools

MongoDB

Kafka

Job description

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.

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
• 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

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.