Description
AI Engineer – 5G RAN Analytics & Root Cause Analysis (Senior Technical Lead)
Role Overview
We are looking for a mid–senior level AI Engineer / Technical Lead (12–16 years overall experience) to architect and build next-generation AI agents for automated Root Cause Analysis (RCA) in our 5G RAN product.
In this role, you will lead the development of agentic AI systems that consume high-volume, high-velocity telecom telemetry data — logs, traces, metrics, events, and KPIs — and autonomously identify, reason about, and explain network issues across the LTE and 5G RAN stack.
This is a hands-on, deeply technical role at the intersection of AI systems engineering, large-scale data engineering, and LTE/5G RAN domain expertise.
What you will do
Key Responsibilities
AI & Agent ArchitectureDesign and implement AI agents for automated RCA across LTE / 5G RAN systems.Build tool-using, reasoning-capable agentic workflows (multi-step analysis, hypothesis testing, causal reasoning).Develop AI pipelines that analyze logs, traces, metrics, events, alarms, and KPIs to detect anomalies and infer root causes.Architect RAG and Graph-RAG based knowledge systems grounded in:Telecom specifications (3GPP)
Product documentation
Historical incidents, playbooks, and RCA reportsContext Engineering & Knowledge SystemsLead context engineering for LLM-based systems (prompt structure, memory, grounding, retrieval boundaries).Design knowledge graphs / causal graphs representing RAN components, signal flows, KPIs, and failure modes.Build explainable AI outputs — human-readable RCA narratives suitable for field engineers and domain experts.
Data Engineering at Telecom Scale
Build and optimize telemetry ingestion pipelines handling terabytes of data:eNB/gNB logs (MAC, PHY, RLC, PDCP, RRC, scheduler, FAPI)Distributed tracesMetrics & time-series KPIsImplement scalable processing using batch + streaming paradigms.Ensure performance, correctness, and cost efficiency for near-real-time analytics.Domain-Driven RCAEncode LTE & 5G RAN domain knowledge into AI-driven analysis:Air-interface failuresScheduling issuesHARQ/BLER/throughput anomaliesMobility, latency, call drop, and QoE degradationCollaborate closely with RAN system engineers and field teams to validate AI diagnoses.Technical LeadershipAct as technical lead / architect for AI-driven observability and RCA initiatives.Perform design reviews, set engineering best practices, and mentor junior engineers.Influence product roadmap for AI-native network analytics.
what you must have
Expert Python programmer (production-grade, scalable systems).Strong data engineering expertise:Large-scale log processingTime-series analyticsDistributed systemsDeep hands-on experience building AI agents (tool-calling, planning, reasoning).AI / ML / LLM SystemsDeep experience with:RAG systemsGraph-RAG / Knowledge-Graph-based retrievalContext engineering and prompt designExperience integrating LLMs into real production systems.Strong understanding of statistics, probability, and data science fundamentals:Anomaly detectionCorrelation vs causationSignal vs noise in noisy telemetry streamsTelecom Domain (Highly Desirable)Strong working knowledge of LTE and/or 5G RAN:MAC, PHY, RLC, PDCP, RRC layersScheduler behavior, HARQ, MIMO, CA, mobilityExperience analyzing RAN logs, traces, KPIs, and counters.Familiarity with 3GPP specifications is a major plus.
Preferred Skills
Experience building AI-driven RCA or observability platforms.Knowledge of causal inference frameworks or graph-based reasoning.Experience with streaming platforms (Kafka, Flink, Spark, etc.).Experience deploying AI systems in cloud-native environments.Exposure to telecom field deployments or live network debugging.Experience Level
12–16 years overall experiencePrior experience as a Senior Engineer / Technical Lead / ArchitectDemonstrated ability to bridge deep domain knowledge with AI systems engineeringWhat Makes This Role Unique
Opportunity to build AI agents that truly reason, not just dashboards or shallow analytics.Direct impact on next-gen autonomous 5G RAN operations.Work on some of the hardest data problems in the telecom domain.Shape the future of AI-native RCA for large-scale communication networks.
