P
Technical Lead, AI Engineering
On-siteFull-timeAI EngineeringTelecommunications
Lead the architecture and hands-on delivery of production AI systems for cellular networks. The role covers agentic AI, machine learning, LLM applications, RAG, model serving, evaluation, and MLOps, while providing technical direction and mentoring from a site in Kfar Saba, Israel.
Responsibilities
- Define end-to-end architectures for AI and machine learning systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure.
- Set technical direction for model, framework, deployment, and build-versus-buy decisions.
- Prototype and implement critical components while establishing engineering and code-quality standards.
- Lead AI solutions from prototype through production, including CI/CD, monitoring, model lifecycle management, versioning, and rollback.
- Create evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data.
- Mentor engineers through architecture discussions, design reviews, and code reviews.
- Work with RAN, PHY, L2/L3, product, and customer-facing teams to turn network challenges into practical AI and machine learning solutions.
- Contribute to technical roadmaps and customer-facing architecture discussions.
Requirements
- At least 7 years of software or machine learning engineering experience, including significant production-system delivery.
- Proven technical leadership and end-to-end ownership of complex system architecture.
- Strong Python skills and hands-on experience with PyTorch or a comparable machine learning framework.
- Practical experience with LLM systems, including agents, tool calling, RAG, orchestration, prompt or context engineering, and evaluation.
- Strong understanding of classical machine learning, 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
- Experience with RAN, wireless infrastructure, telecom operators, or chipset companies.
- Knowledge of O-RAN, RIC, rApps/xApps, and E2, A1, or O1 interfaces.
- Experience with link adaptation, scheduling, radio resource management, channel modeling, or PHY simulation.
- Experience with reinforcement learning or contextual bandits for real-world control problems.
- Experience deploying models in real-time or resource-constrained environments.
- Background in signal processing, communications, or information theory.
- Master’s or doctoral degree in computer science, electrical engineering, applied mathematics, or a related field.