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

Parallelwireless · Bangalore, India · On-site

Python

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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 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


  • 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.
    • Candidarsi ora

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