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.
Skills Required
- 7+ years of experience in software or machine learning engineering, including significant production systems delivery
- Experience providing technical leadership and owning complex system architecture end to end
- Strong Python skills
- Hands-on experience with PyTorch or similar machine learning frameworks
- Practical experience with LLM-based systems, including agents, tool calling, RAG, orchestration, prompt/context engineering, and evaluation
- Strong understanding of classical machine learning, 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
- 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 machine learning models in real-time or resource-constrained environments
- Background in signal processing, communications, or information theory
- M.Sc. or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field
What We Do
At Parallel Wireless, we believe that software has the power to unleash amazing opportunities for the world. We disrupt the ways wireless networks are built and operated. We are reimagining how hardware, software and the cloud work together to change deployment economics for our customers. Our ALL G O-RAN software platform forms an open, secure and intelligent RAN architecture to deliver wireless connectivity, so all people can be connected whenever, wherever, and however they choose. We are engaged with over 50 global MNOs and have been recognized with over 74 industry awards. At the core of what we do is our team of Reimaginers who value innovation, collaboration, openness and customer success. For more information, visit: www.parallelwireless.com.
