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.

Responsibilities

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

Requirements

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