Senior Technical Lead, SMO

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.

• 8+ years of professional software engineering experience building and operating production backend systems.
• Strong proficiency in Go and/or Python.
• Hands-on experience with the Elasticsearch / ELK stack — query DSL, mappings, aggregations, cluster operations, and performance tuning.
• Familiarity with Logstash pipeline development and JVM tuning.
• Production experience with Apache Kafka — topics, partitions, consumer groups, offset management, and scaling.
• Solid Kubernetes and Helm experience; comfortable with containerized deployments and multi-environment value overrides.
• Deep understanding of distributed systems: fault tolerance, backpressure, idempotency, and observability.
• Strong grasp of secure engineering practices — secret management, injection prevention, and least-privilege design.
• 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.

• Design, build, and operate Go microservices (REST/WSS APIs, Kafka consumers, ES sink connectors) and Python data-processing modules.
• Model and tune Elasticsearch stack — mappings, ILM policies, index templates, rollovers — for multi-terabyte telemetry datasets.
• Build and scale Kafka-based streaming pipelines (consumer groups, KEDA autoscaling, dead-letter handling, backpressure).
• Author and maintain Helm charts.
• Develop Kibana dashboards and plugins to surface network insights to operations teams.
• Own reliability, performance, and security of your services in production — including secure secret handling, structured error handling, and graceful failure modes.
• Design logging frameworks for applications to ensure consistent and structured logging practices.
• 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.