Technical Lead, Artificial Intelligence

About the Role

We are building the AI layer of the Radio Access Network, applying agentic AI, machine learning, and LLMs to real RAN challenges — from network operations and root cause analysis to PHY simulation and uplink link adaptation. We are looking for a Technical Lead to own the end-to-end architecture and technical direction of these AI systems. This is a hands-on senior individual contributor role, combining architecture, coding, technical leadership, and mentorship. You will work from our Kfar Saba site alongside RAN, PHY, and software teams, building production-grade AI solutions for real cellular networks. What you'll do: • Define the end-to-end architecture for AI/ML systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure. • Drive technical decisions around models, frameworks, deployment strategies, and build-vs-buy approaches. • Prototype and implement critical components while setting engineering and code-quality standards. • Lead AI solutions from prototype to production, including CI/CD, monitoring, model lifecycle, versioning, and rollback. • Establish evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data. • Mentor engineers through architecture discussions, design reviews, and code reviews. • Collaborate with RAN Systems, PHY, L2/L3, Product, and customer-facing teams to translate network challenges into practical AI/ML solutions. • Contribute to technical roadmap discussions and customer-facing architecture discussions. What you should have: • 7+ years of experience in software or ML engineering, with significant experience delivering production systems. • Proven technical leadership and experience owning the architecture of complex systems end to end. • Strong Python skills and hands-on experience with PyTorch or similar ML frameworks. • Practical experience with LLM-based systems, including agents, tool calling, RAG, orchestration, prompt/context engineering, and evaluation. • Strong understanding of classical ML, including time-series analysis, anomaly detection, and supervised learning. • Working knowledge of 4G/5G RAN architecture, L1/L2/L3, network KPIs, and cellular network operations. • Experience with MLOps, containers, CI/CD, experiment tracking, model monitoring, and production deployment. • Excellent English and strong technical communication skills. Nice to have: • Hands-on experience in RAN, wireless infrastructure, telecom operators, or chipset companies. • Knowledge of O-RAN, RIC, rApps/xApps, and E2/A1/O1 interfaces. • Experience with link adaptation, scheduling, RRM, channel modeling, or PHY simulation. • Experience with reinforcement learning or contextual bandits for real-world control problems. • Experience deploying ML models in real-time or resource-constrained environments. • Background in signal processing, communications, or information theory. • M.Sc. / Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.

Workplace

• Kfar Saba