Senior/Principal RAN Digital Twin & AI Simulation Engineer

• Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin covering LTE, 5G NR, and, where required, 2G/GSM.

• Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations through stable and maintainable interfaces.

• Model the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.

• Extend the current LTE simulation capability and define reusable abstractions that support additional 5G NR and 2G stacks without duplicating the platform.

• Design a fidelity ladder that combines high-fidelity PHY execution with faster calibrated models or lookup/surrogate backends, selecting the least expensive model that is valid for each engineering question.

• Develop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.

• Build representative datasets and experiment pipelines; establish conventional algorithmic baselines; measure accuracy, robustness, generalization, latency, and compute cost before recommending integration into production software.

• Create reproducible A/B experiments across software builds and algorithm versions, using defined scenarios, seeds, configurations, and KPIs such as throughput, BLER/ACK-NACK behavior, MCS, resource-block allocation, SINR, transmit power, latency, and fairness.

• Establish simulation verification and validation practices: matched sim-vs-lab scenarios, calibration rules, lab-repeatability baselines, divergence analysis, model-version tracking, and evidence reports.

• Prevent overfitting the twin to a single setup by separating universal model parameters, setup-specific calibration, and the production algorithms under test.

• Build automated unit, component, end-to-end, regression, and performance tests and integrate them into CI/CD workflows.

• Improve simulation speed, scale, observability, and usability so that stack, PHY, test, and AI engineers can run repeatable experiments independently.

• Debug discrepancies across C/C++, Python, MATLAB, PHY models, production stack behavior, configuration, and reference measurements.

• Document model assumptions, limitations, supported operating regions, calibration provenance, and the validity of every simulation or ML backend.

• Work closely with RAN stack, PHY, system architecture, AI/ML, automation, and lab-validation teams to convert product questions into measurable simulation campaigns.