Technical Lead, Artificial Intelligence

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. - . / Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.