Technical Lead, Artificial Intelligence
at Parallelwireless
Kfar Saba, onsite, senior, 7+ years
posted 15 Sept 2026
- 5G
- PyTorch
- MLOps
- 4G
- CI/CD
- Reinforcement Learning
- RAG
- LLM
- RIC
- Python
- O-RAN
About the role
Posted: 2026-09-15
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 would 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.
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