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

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

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)

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.

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

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.

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

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.