Technical Lead, Artificial Intelligence
Kfar Saba • Full Time
Posted 1mo ago
Job Location
Kfar Saba
Tech Stack
Remote Work Policy
On-site
Employment Type
Full Time
Categories
AI Engineer
About the job
We are building the AI layer of the Radio Access Network, applying agentic AI, machine learning, and LLMs to real RAN challenges. This role is for a Technical Lead to own the end-to-end architecture and technical direction of these AI systems. It is a hands-on senior individual contributor role, combining architecture, coding, technical leadership, and mentorship. You will work 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.