AI Engineer – 5G RAN Analytics & Root Cause Analysis (Senior Technical Lead)

What you will do

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Key Responsibilities
• AI & Agent Architecture
• Design 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 reports
• Context Engineering & Knowledge Systems
• Lead 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 traces
• Metrics & time-series KPIs
• Implement scalable processing using batch + streaming paradigms.
• Ensure performance, correctness, and cost efficiency for near-real-time analytics.
• Domain-Driven RCA
• Encode LTE & 5G RAN domain knowledge into AI-driven analysis:
• Air-interface failures
• Scheduling issues
• HARQ/BLER/throughput anomalies
• Mobility, latency, call drop, and QoE degradation
• Collaborate closely with RAN system engineers and field teams to validate AI diagnoses.
• Technical Leadership
• Act 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

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• Expert Python programmer (production-grade, scalable systems).
• Strong data engineering expertise:
• Large-scale log processing
• Time-series analytics
• Distributed systems
• Deep hands-on experience building AI agents (tool-calling, planning, reasoning).
• AI / ML / LLM Systems
• Deep experience with:
• RAG systems
• Graph-RAG / Knowledge-Graph-based retrieval
• Context engineering and prompt design
• Experience integrating LLMs into real production systems.
• Strong understanding of statistics, probability, and data science fundamentals:
• Anomaly detection
• Correlation vs causation
• Signal vs noise in noisy telemetry streams
• Telecom Domain (Highly Desirable)
• Strong working knowledge of LTE and/or 5G RAN:
• MAC, PHY, RLC, PDCP, RRC layers
• Scheduler behavior, HARQ, MIMO, CA, mobility
• Experience 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 experience
• Prior experience as a Senior Engineer / Technical Lead / Architect
• Demonstrated ability to bridge deep domain knowledge with AI systems engineering
What 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.