System Developer - RAN OAM Management

Company description: Ericsson AB Job description: Join our Team About this opportunity: We are looking for a Master’s graduate with a strong AI/ML background to join our RAN OAM Management team as a System Developer. You’ll work at the frontier of agentic AI, autonomous networks and data engineering – turning raw RAN data into intelligent agents and autonomous functions on live networks. You will join a team of 16 experts owning the RAN OAM system architecture, defining how operators monitor, configure and automate their RAN, and evolving it toward autonomous, AI-driven, self-healing and self-optimizing networks. Previous team members have grown into system architect and technical leadership roles. You won’t just build models or pipelines – you’ll help design the “data nervous system” and agent systems that perceive and act on one of the world’s most complex distributed infrastructures. What you will do: Build and systemize end-to-end AI/ML solutions for RAN operations – from raw network data and feature engineering to models and agents that act on live networks (RAN features, rApps, OAM functions). Design data pipelines and data quality guards that turn heterogeneous RAN/OAM data into reliable, agent-ready and ML-ready datasets. Develop ML/DL models (e.g. time-series forecasting, anomaly detection, root-cause analysis) with solid MLOps (training pipelines, monitoring, drift detection, CI/CD). Contribute to agentic AI for network operations (LLM-based agents, tool use/function calling, multi-agent coordination, human-in-the-loop). Collaborate with RAN/OAM experts to turn operational workflows into autonomous use cases with clear autonomy maturity targets. The skills you bring: Master’s degree in Computer Science, Electrical Engineering, Engineering Physics, Data Science, or similar (recent graduate). Strong ML/AI fundamentals (supervised, unsupervised and reinforcement learning). Experience with time-series forecasting, anomaly detection or root-cause analysis on operational or sensor data. Proficiency in Python and common ML/DL frameworks. Understanding of MLOps basics - training pipelines, monitoring, drift detection and CI/CD for ML. Interest in agentic AI concepts (LLM-based agents, tool use/function calling, prompt engineering). Good to have: Experience designing data pipelines for high-volume data (batch and/or streaming). Familiarity with data quality and observability (schema validation, data contracts, lineage tracking). Experience with distributed data processing (e.g. Spark, Dask, Ray or similar). Telecom domain knowledge or familiarity with Ericsson platforms and autonomous networks frameworks is a plus.